Reservoir Operating Rule Curves Research Articles

An Alternative Approach Using the Firefly Algorithm and a Hybrid Method Based on the Artificial Bee Colony and Cultural Algorithm for Reservoir Operation

In reservoir operation rule curves, it is necessary to apply rule curves to guide long-term reservoir management. This study proposes an approach to optimizing reservoir operation rule curves (RORCs) using intelligent optimization techniques from the firefly algorithm (FA) and a unique combination method utilizing the artificial bee colony and cultural algorithm (ABC-CA). The aim is to establish a connection with the simulation model to determine the optimal RORCs for flood control. The proposed model was used to determine the optimal flood control RORC for the Nam-Oon Reservoir (NOR) in northeastern Thailand. A minimum frequency and minimum average of excess water were provided as an objective function for assessing the efficiency of the search process. The evaluation of the effectiveness of flood control RORCs involved expressing water scarcity and excess water situations in terms of frequency, magnitude, and duration using historical inflow data synthesized from 1000 events. The results demonstrated that when using the obtained RORC to simulate the NOR system for reducing flooding in long-term operations, excess water scenarios were smaller than those using the current RORC. The results showed that the excess water scenario using the RORC obtained from the proposed model can reduce the excess water better than the current RORC usage scenario. In decreasing flood situations, the newly acquired RORC from the suggested FA and ABC-CA models performed better than the current RORC.

Study on the Basic Form of Reservoir Operation Rule Curves for Water Supply and Power Generation

Reservoir operation rule curves are crucial for managing water supply and power generation in reservoirs. As the number of objectives and management requirements increase, there is a growing demand for optimized operation rule curves. The objective of this study is to explore the most effective forms of reservoir operation rule curves, focusing on the case of the Nierji Reservoir and considering the dual objectives of water supply and power generation. The parameter–simulation–optimization framework, specifically employing the NSGA-II algorithm, was used to analyze and compare two basic forms of operation rule curves: the shared type and independent type. The impact of these curves on water supply potential and multi-objective optimization results with various water demand scenarios was assessed. The analysis revealed that the choice of operation rule curve form can influence the maximum water supply potential of the reservoir to some extent. The independent type operation rule curve was significantly more effective in enhancing the water supply potential for industrial and domestic users, resulting in a notable increase of 3.5 × 108 m3. Additionally, it also proved beneficial for environmental users, with an increase of 1 × 108 m3. Conversely, the shared type operation rule curve demonstrated similar functionality to the independent type curve with fewer decision variables, particularly when the water demand was relatively low. In scenarios with high water demand, the independent type curve outperformed the shared type curve by generating 6549 superior, non-dominated solutions for multi-objective optimization, specifically focused on maximizing reservoir operation benefits. In conclusion, selecting the appropriate form of reservoir operation rule curve is crucial to balance different reservoir functional objectives and achieve optimal results. Further research could focus on quantifying the specific benefits and trade-offs associated with each type of curve in order to provide more robust evidence for the advantages of a complex reservoir system.

Climate-associated risks for reliability, resilience, vulnerability and sustainability of irrigation reservoirs in small ungauged watersheds

Climate change poses an existential threat to the functional integrity of the network of agricultural reservoirs responsible for sustaining rice production in South Korea. Anthropogenic interventions have triggered a cycle of droughts and floods by magnifying the already existing supply–demand gap, which is seriously hampering reservoir performance. The watersheds of most agricultural reservoirs in South Korea are ungauged, and inflow and outflow data are not measured. In this study, recorded volumetric water storage data from five reservoirs were used to derive observed inflow data based on the hydrological reasoning and reservoir operation rule curves. These data were then used to evaluate the performance of a lumped rainfall-runoff model in simulating the runoff generated in upstream ungauged watersheds. A water balance analysis was conducted for 400 major agricultural reservoirs spread throughout the country during 1973–2017. Performance indices were estimated based on inflow availability in the reservoirs and downstream irrigation demands. Empirical relationships were derived to predict performance indices by using the reservoirs’ physical characteristics as input variables. Generally, reservoirs received substantially higher inflows compared to irrigation demands, suggesting sufficient water availability during the rice growing season (Apr−Sep). An out-of-phase cycle of peak inflow generation and water demand periods induced frequent failure events during the rice nursery transplanting period (May−Jun), and reservoir sustainability was minimal in these months. Southern and southwestern major agricultural regions with a higher density of small- to medium-sized reservoirs would be at higher risk of climate-induced water scarcity. Upsizing storage capacity could alleviate the water deficit in reservoirs associated with watersheds featuring higher means and lower standard deviations of runoff. A watershed-to-irrigated area ratio > 5 indicated acceptable reservoir sustainability.

Optimal Operation of Nashe Hydropower Reservoir under Land Use Land Cover Change in Blue Nile River Basin

Changes in LULC (land use land cover), which significantly influence the spatial and temporal distribution of hydrological processes and water resources in general, have a substantial impact on hydropower generation. The utilization of an optimization approach in order to analyze the operation of reservoirs is an important concern in the planning and management of water resources. The SWAT (Soil and Water Assessment Tool) and the HEC-ResPRM (Hydrologic Engineering Center reservoir evaluation system Prescriptive Reservoir Model) were combined to model and optimize the Nashe hydropower reservoir operation in the Blue Nile River Basin (BNRB). The stream flow into the reservoir was determined using the SWAT model, considering the current and future impacts of LULC changes. The HEC-ResPRM model has been utilized in order to generate the optimal hydropower reservoir operation by using the results of the SWAT calibrated and validated stream flow as input data. This study proposes a method for integrating the HEC-ResPRM and SWAT models to examine the effects of historical and future land use land cover change on the watershed’s hydrological processes and reservoir operation. Therefore, the study aimed to investigate the current and future optimal reservoir operation scenarios for water resources management concerning hydropower generation under the effect of LULC changes. The results reveal that both the 2035 and 2050 LULC change scenarios show the increased operation of hydropower reservoirs with increasing reservoir inflows, releases, storage, and reservoir elevation in the future. The effects of LULC change on the study area’s hydrological components reveal an increase in surface runoff until 2035, and its decrease from 2035 to 2050. The average annual reservoir storage and elevation in the 2050 LULC scenario increased by 7.25% and 2.27%, respectively, when compared to the current optimized scenario. Therefore, changes in LULC have a significant effect on hydropower development by changing the total annual and monthly reservoir inflow volumes and their seasonal distribution. Reservoir operating rule curves have been commonly implemented in the operation of hydropower reservoirs, since they help operators to make essential, optimal decisions with available stream flow. Moreover, the generated future reservoir rule curves can be utilized as a reference for the long-term prediction of hydropower generation capacity, and assist concerned authorities in the successful operation of the reservoir under the impact of LULC changes.

Hybrid methods for reservoir operation rule curve determination considering uncertain future condition

In this research, simple and practical methods have been proposed to determine the reservoir operation rule curves considering uncertain water inflow values at future conditions based on the concept of comparative reservoirs management. For this purpose, two methods are proposed. In the first method named His-Inf, by considering the historical water inflow values, a mathematical optimization model is proposed for defining the reservoir operation rule curves determination problem and solved using nonlinear programming (NLP) method. However, in the second method, three different approaches named Pre-Inf1, Pre-Inf2 and Pre-Inf3 are proposed. In the Pre-Inf1 method, at first, water inflow values into the dam reservoir are predicted using neural network (ANN) model. Then, the predicted water inflow values are used to determine and update the reservoir operation rule curves using NLP method. In the Pre-Inf2 method, the ANN model is used to simulate and update the constant coefficients of the reservoir operation rule curves considering the water demand, water inflow values into the reservoir, water release values form reservoir and time index (T) as data set. For this purpose, two different framework and structure are proposed for sorting and providing data set leading to two different cases. Finally, in the Pre-Inf3 method, at first, water inflow values, reservoir storage volumes, water demand values and time index (T) are used to simulate and predict water releases values from the reservoir. Then, the linear regression method is used to determine the reservoir operation rule curves and policies. Here, both static and dynamic ANN models are used for simulating and predicting the water inflow values and reservoir operation rule curves and policies. In order to investigate the performance of proposed methods, here, the Sefidrood dam reservoir is considered as a case study in which it is located at the intersection of the QezelOzan River and Shahroud River near the city of Manjil in the north of Iran. Finally, the obtained results are presented and compared by calculating the reliability, residency, vulnerability and sustainability indexes. Comparison of the result indicates the superiority of the proposed Pre-Inf3 method for determining the reservoir operation rule curves at uncertain future condition in which the results of the His-Inf, Pre-Inf1 and Pre-Inf2 methods are almost similar. In other words, the sustainability index of His-Inf, Pre-Inf1, Pre-Inf2 and Pre-Inf3 are 63%, 63%, 63% and 74%, respectively, in which the corresponding value of Pre-Inf3 is 17.5% bigger than other proposed methods.

Investigation of a composite two-phase hedging rule policy for a multi reservoir system using streamflow forecast

Long-term changes in reservoir inflow due to climate changes and human interference violate the assumptions of hydrologic stationarity especially in the reservoir design. Utilization of uncertain prediction into a reservoir operating rule curves somehow reflects the challenges that imposed by nonstationary conditions. This study proposes a hedging based policy incorporated forecast term to manage release decisions in two separate phases. Hedging is applied firstly regarding to reservoir water level similar to conventional hedging rules and secondary according to an extra simulation in the near future. To determine the time interval of future effects, an exterior optimization model is introduced to handle the trade-off between forecast uncertainty and future information which imposed by forecast horizon. Future inflows are forecasted introducing a model including a wrapper-based feature selection method and AdaBoost.RT as a learning algorithm. The results of applying the model to a real six reservoir system in IRAN showed that incorporating future inflows into the real time decisions significantly improves the total squared relative deficit about 20% and 10% compared to conventional hedging rule curve (CHRC) and standard operation policy as objective function. Also having a glance at the near future reduces the vulnerability of the system about 5% and 27% respectively against CHRC and SOP. The results also showed that, although the SOP reaches to a best reliability of satisfying water demands in total system as 31% and 27% better than CHRC and the proposed two-phase policy, but the number of intensified failures was higher than two others which somehow influences on volume-based indices like vulnerability.

Reservoir ecological operation by quantifying outflow disturbance to aquatic community dynamics

Reservoir operation causes spatiotemporal variations in outflow, which influence the dynamics of downstream aquatic communities. However, empirical evidence of community responses to hydrological alteration remains limited for dam-regulated rivers. This study focused on quantifying the streamflow disturbance to multi-population dynamics in downstream of the China’s Danjiangkou Reservoir. First, the stochastic population dynamics model (PDM) was used to simulate aquatic community dynamics. Then, the flow–ecology relationship was established to identify community response to reservoir outflow. Third, two novel ecological indicators, stable time (ST) and coefficient of variation at stable time (CVST), were proposed to evaluate the resilience and resistance of multi-population systems, respectively. Finally, the reservoir operating rule curves were optimized by considering tradeoffs between socioeconomic and ecological objectives. The coevolution processes of multi-population systems (fish, phytoplankton, zooplankton, zoobenthos, and macrophytes) were simulated by stochastic PDMs. The population densities of stable states showed continuous downward trends with increasing degree of hydrological alteration for multi-population systems, and aquatic community systems could be destroyed when alteration reached its acceptable maximum. The greater the degree of hydrological alteration, the longer the recovery time from an unstable to a stable state, and the weaker resistance for each population system. The resilience and resistance of downstream multi-population systems were enhanced by optimizing reservoir outflow. The optimization results illustrated that the performances of the multiple objectives of water supply, hydropower generation, and ST were improved by 2.37%, 2.40%, and 2.67%, respectively, whereas the performance of CVST was the same as the conventional operation. The flow–ecology relationship provided an approach to quantify the impacts of reservoir outflow on an aquatic community, which is helpful in guiding ecological flow strategies.

An integrated framework for optimizing large hydro–photovoltaic hybrid energy systems: Capacity planning and operations management

Synchronous resolution of capacity planning and operations management sub-problems of large hybrid energy systems (HESs) may further enhance the synergy between different energy sources. However, these two sub-problems constitute a complex bi-level programming problem, which is difficult to effectively solve. In this study, we proposed an effective framework to synchronously determine the optimal photovoltaic (PV) size and long-term operating rules for large hydro–PV HESs. Firstly, a set of short-term operation models for each possible PV size were established to simulate joint operation processes of hydropower and PV power. Then, PV-energy loss functions were derived based on short-term simulation results and they were subsequently incorporated into long-term optimal operation models, aiming to maximize net energy production of the HES. Thirdly, long-term operating strategies were optimized by dynamic programing and sampled using a bootstrap method to generate inputs for a capacity planning model. Finally, the optimal PV size was determined by maximizing the net present value of the PV plant over its life-span, while operating rules were obtained using a regression analysis method. A case study was carried out using China’s Longyangxia hydro–PV HES. The results showed that: (1) the integrated framework could synchronously yield optimal PV size and operating rules for the HES without heavy computational burden; (2) the capacity planning model could provide an optimal PV size of 880 MW with a confidence interval of [820, 930] MW; and (3) the derived operating rules outperformed the conventional reservoir operating rule curves in terms of power generation and water supply performances. Therefore, the proposed framework can be an effective tool for the planning and management of large hydro–PV HESs.

Reservoir ecological operation considering outflow variations across different time scales

The regulation of reservoirs contributes huge socioeconomic benefits to society at the expense of inducing adverse effects on downstream ecosystems. The reservoir average outflow (such as monthly) can be out of the ecological flow range while the partial daily outflow is suitable due to the outflow fluctuations. To address this issue, the reservoir outflow variations across different time scales (OVDTS) were proposed to form a novel ecological indicator. First, the ecological flow for the full life cycles of indicative native and nonnative species was calculated. Then, the reservoir OVDTS were proposed to describe the streamflow difference between long (e.g., 10-day) and short (e.g., daily) time intervals, and the ecological fitness (namely ecofit) was then formed. Finally, the reservoir operating rule curves were optimized by considering ecofit as well as average annual water supply, water supply reliability, average annual hydropower generation and hydropower generation reliability. The China’s Danjiangkou Reservoir was selected as the case study. It is validated that the established ecofit was almost equal to the proportion of reservoir outflow that met ecological flow range. The ecofit of optimized operating rule curves was improved by 4.19% compared with conventional operation without decreasing the performance of other objectives, also with ecofit improvements of wet, normal, and dry years. It was unexpected that the ecological objective of the optimized operating rule curves was significantly better than that of natural flow mimicry in dry years. The proposed OVDTS and ecofit could be helpful to reservoir ecological operations.

Applying wavelet transformation and artificial neural networks to develop forecasting-based reservoir operating rule curves

ABSTRACT In order to provide more accurate reservoir-operating policies, this study attempts to implement effective monthly forecasting models. Seven inflow forecasting schemes, applying discrete wavelet transformation and artificial neural networks are proposed and provided to forecast the monthly inflow of Dez Reservoir. Based on some different performance indicators the best scheme is achieved comparing to the observed data. The best forecasting model is coupled with a simulation-optimization framework, in which the performance of five different reservoir rule curves can be compared. Three applied rules are based on conventional Standard operation policy, Regression rules, and Hedging rule, and two others are forecasting-based regression and hedging rules. The results indicate that forecasting-based operating rule curves are superior to the conventional rules if the forecasting scheme provides results accurately. Moreover, it can be concluded that the time series decomposition of the observed data enhances the accuracy of the forecasting results efficiently.

Reducing lake water-level decline by optimizing reservoir operating rule curves: A case study of the Three Gorges Reservoir and the Dongting Lake

Water levels of a lake are critical for functions such as navigation, water supply and ecological services; however, the lake water level can be reduced significantly by upstream reservoir refill operations. In this study, the reservoir operating rule curves were redesigned to satisfy the lake’s water demand. Variations in lake water level in response to reservoir releases upstream are simulated by using a river-lake model, which is simple and less time-consuming than hydrodynamic models. In the river-lake model, inflow to the lake and outflow from the lake are derived by using a series of regression models of upstream reservoir releases. Then optimization operation model is developed to minimize the lake water-level decline with the restored water level as the benchmark, and to maximize the benefits of hydropower generation simultaneously. The NSGA-II algorithm is used to solve the optimization model, where a moving-average filter is embedded to smooth the operating rule curves. China’s Dongting Lake and its upstream reservoir, the Three Gorges Reservoir were selected as a case study. The proposed river–lake model is effective for simulating the lake water level variations, with a Nash–Sutcliffe Efficiency coefficient of 0.94. With the derived optimal operating rule curves, the lake water-level decline caused by conventional reservoir operation during the refill period is reduced by 5.0%, and the hydropower generation and hydropower reliability are improved by 3.9% and 8.3%, respectively. Therefore, the proposed method is an effective tradeoff between lake restoration and enhancing the benefits of hydropower.

Revisiting Water Supply Rule Curves with Hedging Theory for Climate Change Adaptation

Conventional reservoir operation rule curves are based on the assumption of hydrological stationarity. The aggravating non-stationarity under the changing environment rocked this foundation. The hedging theory is one of the options for adaptive operation based on hydrological forecasts, which can provide a practical tool for optimal reservoir operation under a changing environment. However, the connections between hedging theory and rule curves are not clear. This paper establishes the linkage of rule curves and hedging theory by analyzing three fundamental problems surrounding the design of conventional rule curves, namely the law and design of water supply rule curves, the determination of flood control storage, and the division of refill and drawdown circle. The general interpretation of the conventional water supply rule curves with hedging theory is conducted. Both the theoretical analyses and the Danjiangkou Reservoir case study reveal that, based on the historical records, the rule curves can be interpreted as a specific expression of hedging theory. This intrinsic linkage allows us to propose a more general and scientific method of updating rule curves in the context of non-stationarity. On this basis, the rule-curve-based climate adaptation strategies are figured out using hedging theory. This research is helpful for practical adaptive operation of reservoirs in the changing environment.

Development of an optimal reservoir pumping operation for adaptation to climate change

Optimization of operation rule curve of Layang reservoir located in southern part of peninsular Malaysia under climate change uncertainties has been presented in this study. Downscaled climate data were incorporated in a lumped rainfall-runoff model to project daily reservoir inflow, which was then used to determine the reservoir operation rule curves to minimize the operation cost. The results reveal that climatic change will cause a rise of temperature in the range of 1.5-2.9°C, but decrease in rainfall in most of the months in the end of this century which will casue a change in the seasonal pattern of inflow to reservoir. Comparison of historical and model generated operation costs indicates that annual savings of 23% and 33% is possible with optimized reservoir operation rule curves. Assessment of the performance of the optimized operation curve under climate change scenarios shows that it will be able to cope with the changing pattern of reservoir inflow with proper adjustment. It is expected that the method presented in this paper to derive pumping operation curves to optimize the total volume of water to be pumped from various sources to reservoir can be useful for reservoir operators in the context of climate change.

Optimizing water supply and hydropower reservoir operation rule curves: An imperialist competitive algorithm approach

Efficient reservoir management requires the implementation of generalized optimal operating policies that manage storage volumes and releases while optimizing a single objective or multiple objectives. Reservoir operating rules stipulate the actions that should be taken under the current state of the system. This study develops a set of piecewise linear operating rule curves for water supply and hydropower reservoirs, employing an imperialist competitive algorithm in a parameterization–simulation–optimization approach. The adaptive penalty method is used for constraint handling and proved to work efficiently in the proposed scheme. Its performance is tested deriving an operation rule for the Dez reservoir in Iran. The proposed modelling scheme converged to near-optimal solutions efficiently in the case examples. It was shown that the proposed optimum piecewise linear rule may perform quite well in reservoir operation optimization as the operating period extends from very short to fairly long periods.

Optimized cascade reservoir operation considering ice flood control and power generation

Summary Ice flood control is an important objective for reservoir operation in cold regions. Maintaining the reservoir outflow in a certain range is considered an effective way to remediate ice flood damage. However, this strategy may decrease the socio-economic benefit of reservoirs, for example, reduction of hydropower production. These conflicting objectives cause a dilemma for water managers when defining reservoir operation policy. This study considers seven cascade reservoirs in the upstream Yellow River, and ice flood control storage is introduced to balance the hydropower generation and ice flood control. The relation between the ice flood control storage volume of the Liujiaxia reservoir and cascade power output is analyzed. An optimization model to explore the trade-offs between hydropower generation and ice flood control requirements is developed. The model takes into account ice flood control requirements. The optimization model compared to simulation model based on the reservoir operation rule curves. The results show that the optimal operation rules are far more efficient in balancing the benefits within the power generation and ice flood control. The cascade reservoirs operation strategies proposed in this study can be effectively and suitably used in reservoir operation systems with similar conditions.

A reservoir operating method for riverine ecosystem protection, reservoir sedimentation control and water supply

• Water level and sediment volume are triggers of sediment flushing. • Reservoir operating rule curves are changed from “static” to “dynamic” ones. • Both the flow and sediment regimes are considered for river protection. Riverine ecosystem protection requires the maintenance of natural flow and sediment regimes downstream from dams. In reservoir management schedules this requirement should be integrated with sedimentation control and human water supply. However, traditional eco-friendly reservoir operating methods have usually only considered the natural flow regime. This paper seeks to develop a reservoir operating method that accounts for both the natural flow and sediment regimes as well as optimizing the water supply allocations. Herein, reservoir water level (RWL), sediment-occupied ratio of reservoir volume (SOR) and rate of change of SOR (RCSOR) are adopted as three triggers of a drawdown-flushing-based sediment management policy. Two different groups of reservoir operating rule curves (RORCs) are designed for sediment-flushing and non-sediment-flushing years, and the three triggers, RWL, SOR and RCSOR, are used to change the “static” RORCs to “dynamic” ones. The approach is applied to the Wangkuai Reservoir, China to test its effectiveness. This shows that the approach can improve the flexibility of reservoir operators to balance the reservoir management, water supply management and the flow and sediment needs of the downstream riverine ecosystem.

Coupled-operations model and a conditional differential evolution algorithm for improving reservoir management

This paper proposes a flexible methodology for finding the optimal control of reservoir operations, which is adopted for simulating a storage system including a large reservoir simulation model and a numerical search method for searching decision variables. The optimization model used was a conditional differential evolution algorithm (CDE). The model was connected to reservoir simulation model for searching the decision variables in reservoir management. The reservoir management model was applied to Lampao reservoir in Kalasin province located in the northeast of Thailand. This study considers the criteria of minimum water shortages and downstream flood control. There were synthetic inflow data for evaluating the performance of the reservoir operating rule curves. It was found that the reservoir operating rule curves from the CDE showed a lower frequency and quantity of water shortage than the existing reservoir operating rule curve. In addition, the flood frequency and the flood quantity were reduced. The results also demonstrated that the CDE provided better reservoir operation in any probability of inflow situations. Further, it indicated that the reservoir operating rule curves obtained from this research could effectively support the increasing water requirements to a growing population. Key words: A conditional differential evolution algorithm (CDE), simulation model, optimization technique, reservoir rule curves

Incorporating ecological requirement into multipurpose reservoir operating rule curves for adaptation to climate change

Summary Operating rule curves have been widely applied to reservoir operation, due to their ease of implementation. However, these curves excluding ecological requirement are generally derived from observed or synthetic flows and have rarely been determined by future flows under climate change. This paper develops an integrated adaptive optimization model (IAOM) for derivation of multipurpose reservoir operating rule curves including ecological operating rule curve under future climate change. Steps in the proposed IAOM include: (1) weather generator module to generate feasible future climate conditions, (2) VIC model as the hydrological simulation module to generate streamflows from those future weather conditions, and (3) multipurpose reservoir optimization module to determine the optimal reservoir operations to deal with climate change. China’s Danjiangkou reservoir in Han River basin is selected for a case study. The results demonstrate that the IAOM provides optimal multipurpose reservoir operating rule curves that reflect the hydrologic characteristics of future climate change. Ecological supply water operation will alleviate negative effect of dam on river ecosystem without reducing conservation benefits and flood control standard. Therefore, they can consult with reservoir administrators if it is useful results for operations.

Optimization of Water Diversion Based on Reservoir Operating Rules: Analysis of the Biliu River Reservoir, China

AbstractInterbasin water-transfer projects are constructed to solve regional water-shortage problems. This study attempts to develop a set of water-diversion strategies to assist in the decision-making process of effectively diverting the water. The reservoir operation rule curves are improved by considering water-diversion rule curves that consist of hydrological-stage and water-level factors. A set of water-diversion strategies consisting of diversion rule curves, diversion flows, and supply rules is then developed to guide the actual water diversion. To minimize total water supply shortages of the urban, agricultural, and ecological water demands, the optimization model is constructed and the variables of diversion strategy are optimized by genetic algorithm. Furthermore, three scenarios including scenarios of no water diversion, water diversion with constant flow, and diversion with strategy are used to better understand the advantages of applying diversion strategies. The authors present a case study...

Integrated Reservoir Management System for Flood Risk Assessment Under Climate Change

Operations of existing reservoirs will be affected by climate change. Reservoir operating rules developed using historical information will not provide the optimal use of storage under changing hydrological conditions. In this paper, an integrated reservoir management system has been developed to adapt existing reservoir operations to changing climatic conditions. The reservoir management system integrates: (1) the K-Nearest Neighbor (K-NN) weather generator model; (2) the HEC-HMS hydrological model; and (3) the Differential Evolution (DE) optimization model. Six future weather scenarios are employed to verify the integrated reservoir management system using Upper Thames River basin in Canada as a case study. The results demonstrate that the integrated system provides optimal reservoir operation rule curves that reflect the hydrologic characteristics of future climate scenarios. Therefore, they may be useful for the development of reservoir climate change adaptation strategy.