Multi-Objective Operating Rules for Danjiangkou Reservoir Under Climate Change

The natural variations of climatic system, as well as the potential influence of human activity on global warming, have changed the hydrologic cycle and threatened current water resources management. And the conflicts between different objectives in reservoir operation may become more and more challenging because of the impact of climate change. This study aims at deriving multi-objective operating rules to adapt to climate change and alleviate the conflicts. By combining the reservoir operation function and operating rule curves, an adaptive multi-objective operation model was proposed and developed. The optimal operating rules derived both by dynamic programming and NSGA-II method were compared and discussed. The projection pursuit method was used to select the best operating rules. The results demonstrate that the reservoir operating rules obtained by NSGA-II can increase the power generation and water supply yield and reliability, and the rules focusing on water supply can significantly increase the reservoir annual water supply yield (by 18.7 %). It is shown that the proposed model would be effective in reservoir operation under climate change.     

变化环境下水库适应性调度研究进展与展望

在气候变化与人类活动共同驱动的变化环境下,依赖于一致性水文条件所设计的传统水库调度运行策略,将难以满足决策者的需求。为了保障水资源安全与高效利用,水库管理者需对传统水库调度运行策略进行适应性调整。因此,变化环境下水库适应性调度作为当前水库调度领域的一项前沿课题,国内外专家学者已经开展了大量卓越的工作。本文旨在于总结近年来的水库适应性调度研究进展,包括变化环境下水库入库径流预测、水库调度规则编制以及耦合变化环境-入库径流-调度规则的框架等方面,并归纳当前相关研究中存在的问题及不足。进而,未来水库适应性调度研究发展方向,建议更多关注考虑自然-人工互馈影响的入库径流预测、水库调度运行策略的静态衔接与动态调整。     

Adaptive Reservoir Management by Reforming the Zone-based Hedging Rules against Multi-year Droughts

In this study, the zone-based hedging rule, which is the main operating policy adopted from multipurpose reservoirs in Korea is adjusted to reflect the multi-year droughts caused by climate change. Annual synthetic inflow series with different magnitudes of long memory were generated using the autoregressive fractional integrated moving average (ARFIMA) model. The generated inflow series were then disaggregated into 10-day series and utilized as input variables to derive the alternative hedging rules. The alternative hedging rules from this study were used in adaptive reservoir management by newly updated information. Finally, the performance of the suggested policy is measured in terms of frequency and magnitude under the historical inflow series. As a result, adaptive reservoir management demonstrated improvements in the following terms of the frequency of critical failures (water deficit ratio greater than 30%): 6.14% of the simulation period in the status quo (SQ) policy, and 2.99% in the adaptive management. However, the overall reliability of the reservoir during the simulation horizon was better when operated with the SQ policy (41.19%) than the results from adaptive management (26.42%). Because this result is in a good agreement with the original objective of the hedging rules, the adaptive policy suggested in this study holds promise and may be utilized in further reservoir management with an increase of potential drought risk from climate change.

     

Investigation of the Behavior of an Agricultural-Operated Dam Reservoir Under RCP Scenarios of AR5-IPCC

In regions where the Mediterranean climate prevails, the agricultural sector and agricultural-operated dam reservoirs are threatened by climate change. In this respect, the prediction of hydro-meteorological changes that may occur in surface water resources under climate change scenarios is essential to examine the sustainability of reservoirs. In this paper, Demirköprü reservoir in the Gediz Basin/Turkey, a reservoir operated for irrigation purposes, was analyzed against the RCP4.5 and RCP 8.5 scenarios specified in the AR5 report of the IPCC. Projection period was evaluated as 2016-2050 water year period. First, statistical downscaling, Bayesian model averaging and quantile delta mapping bias correction techniques were respectively applied to monthly total precipitation and monthly average temperatures of meteorological stations in the region using 12 GCMs. According to RCP4.5 and RCP8.5, negligible reductions in precipitation are foreseen, while significant increases of 1.3 and 1.8 °C, respectively, are projected for temperatures under the same scenarios. Following the calibration of rainfall-runoff models for the sub-basins feeding the reservoir, streamflow simulations were also performed with projected precipitation and temperatures. In particular, according to the RCP 8.5 scenario, reservoir inflows during the period 2016-2050 could be reduced by 21% compared to the reference scenario results. Finally, the projected crop water demands and hydro-meteorological changes are evaluated together and the reservoir performances are examined using various indices. Assuming that the performance of the past irrigation yields will not change in the future, it is foreseen that reservoir’s sustainability will decrease by 16% under the RCP8.5 scenario. Even if the irrigation efficiency is increased by 40%, the reservoir cannot reach past sustainability characteristics.     

Water Management of Irrigation Dams Considering Climate Variation: Case Study of Zayandeh-rud Reservoir, Iran

Dependency of reservoir operation on the climate variation occurs especially in regions, where agricultural demand has a significant share of the total water demands. The variability between demands that are based on annual climate conditions may be larger than the uncertainty associated with other explanatory variables in long-term operation of an irrigation dam. This paper proposed a rule curves to the water managers of the Zayandeh-rud reservoir in Iran in long-lead reservoir operation. A regional optimal allocation of water among different crops and irrigation units is developed. The optimal allocation model is coupled with a reservoir operating model, which is developed based on the certain hedging that deals with the available water and the water demands mutually. This coupled model is able to activate restrictions on allocating water to agricultural demands considering variation of inflow to the reservoir, variation of demands and the economic value of allocating water among different crops and irrigation units. The resulted rule curve is presented with a number of tables for more details and accuracy and a simple curve, which is more useful for operational purpose.     

Development of a Demand Driven Hydro-climatic Model for Drought Planning

In recent years, droughts with increasing severity and frequency have been experienced around the world due to climate change effects. Water planning and management during droughts needs to deal with water demand variability, uncertainties in streamflow prediction, conflicts over water resources allocation, and the absence of necessary emergency schemes in drought situations. Reservoirs could play an important role in drought mitigation; therefore, development of an algorithm for operation of reservoirs in drought periods could help to mitigate the drought impacts by reducing the expected water shortages. For this purpose, the probable drought’s characteristics and their variations in response to factors such as climate change should be incorporated. This study aims at developing a contingency planning scheme for operation of reservoirs in drought periods using hedging rules with the objective of decreasing the maximum water deficit. The case study for evaluation of the performance of the proposed algorithm is the Sattarkhan reservoir in the Aharchay watershed, located in the northwestern part of Iran. The trend evaluations of the hydro-climatic variables show that the climate change has already affected streamflow in the region and has increased water scarcity and drought severity. To incorporate the climate change study in reservoir planning; streamflow should be simulated under climate change impacts. For this purpose, the climatic variables including temperature and precipitation in the future under climate change impacts are simulated using downscaled GCM (General Circulation Model) outputs to derive scenarios for possible future drought events. Then a hydrological model is developed to simulate the river streamflow, based on the downscaled data. The results show that the proposed methodology leads to less water deficit and decreases the drought damages in the study area.     

Single Reservoir Operating Policies Using Genetic Algorithm

To obtain optimal operating rules for storage reservoirs, large numbers of simulation and optimization models have been developed over the past several decades, which vary significantly in their mechanisms and applications. As every model has its own limitations, the selection of appropriate model for derivation of reservoir operating rule curves is difficult and most often there is a scope for further improvement as the model selection depends on data available. Hence, evaluation and modifications related to the reservoir operation remain classical. In the present study a Genetic Algorithm model has been developed and applied to Pechiparai reservoir in Tamil Nadu, India to derive the optimal operational strategies. The objective function is set to minimize the annual sum of squared deviation form desired irrigation release and desired storage volume. The decision variables are release for irrigation and other demands (industrial and municipal demands), from the reservoir. Since the rule curves are derived through random search it is found that the releases are same as that of demand requirements. Hence based on the present case study it is concluded that GA model could perform better if applied in real world operation of the reservoir.     

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.     

Deriving Reservoir Refill Operating Rules by Using the Proposed DPNS Model

The dynamic programming neural-network simplex (DPNS) model, which is aimed at making some improvements to the dynamic programming neural-network (DPN) model, is proposed and used to derive refill operating rules in reservoir planning and management. The DPNS model consists of three stages. First, the training data set (reservoir optimal sequences of releases) is searched by using the dynamic programming (DP) model to solve the deterministic refill operation problem. Second, with the training data set obtained, the artificial neural network (ANN) model representing the operating rules is trained through back-propagation (BP) algorithm. These two stages construct the standard DPN model. The third stage of DPNS is proposed to refine the operating rules through simulation-based optimization. By choosing maximum the hydropower generation as objective function, a nonlinear programming technique, Simplex method, is used to refine the final output of the DPN model. Both the DPNS and DPN models are used to derive operating rules for the real time refill operation of the Three Gorges Reservoir (TGR) for the year of 2007. It is shown that the DPNS model can improve not only the probability of refill but also the mean hydropower generation when compare with that of the DPN model. It's recommended that the objective function of ANN approach for deriving refill operating rules should maximize the yield or minimize the loss, which can be computed from reservoir simulation during the refill period, rather than to fit the optimal data set as well as possible. And the derivation of optimal or near-optimal operating rules can be carried out effectively and efficiently using the proposed DPNS model.     

Adapting Multireservoir Operation to Shifting Patterns of Water Supply and Demand

The aim of this study is to determine whether dam reoperation (the adjustment of reservoir operating rules) is an effective adaptation strategy to reduce the potential impacts of climate change and regional socio-economic developments. The Xinanjiang-Fuchunjiang reservoir cascade, located in Hangzhou Region (China), is selected as case study. We use a scenario-based approach to explore the effects of various likely degrees of water stress for the future period between 2011 and 2040, which are compared to the control period from 1971 to 2000. The scenario impacts are simulated with the WEAP water allocation model, which is interlinked with the NSGA-II metaheuristic algorithm in order to derive optimal operating rules adapted to each scenario. Reservoir performance is measured with the Shortage Index (SI) and Mean Annual Energy Production (MAEP). For the investigated scenarios, adapted operating rules on average reduce the SI with 84 % and increase the MAEP with 6.4 % (compared to the projected future performance of conventional operation). Based on the optimization results, we conclude that for the studied case dam reoperation is an effective adaptation strategy to reduce the impact of changing patterns of water supply and demand, even though it is insufficient to completely restore system performance to that of the control period.     

Structural and Non-Structural Climate Change Adaptation Strategies for the Péribonka Water Resource System

This paper discusses the possibility for a privately managed hydro-power system to adapt to a projected increase in water flow in their central-Québec watersheds by adding power generation potential. Runoffs simulated by a lumped rainfall-runoff model were fed into a stochastic dynamic programming (SDP) routine to generate reservoir operating rules. These rules were optimized for maximum power generation under maximal and minimal reservoir level constraints. With these optimized rules, a power generation simulator was used to predict the amount of generated hydropower. The same steps, excluding calibration, were performed on 60 climate projections (from 23 general circulation models and 3 greenhouse gas emission scenarios) for future horizons 2036–2065 and 2071–2100. Reservoir operation rules were optimized for every climate change projection for the 3 power plants in the system. From these simulations, it was possible to determine hydropower numbers for both horizons. The same steps were performed under a modified system in which an additional turbine was added to each power plant. Results show that both the non-structural (optimizing reservoir rules) and structural (adding turbines) adaptation measures allow for increased power production, but that adapting operating rules is sufficient to reap the most of the benefits of increased water availability.     

Streamflow Forecast and Reservoir Operation Performance Assessment Under Climate Change

This study attempts to investigate potential impacts of future climate change on streamflow and reservoir operation performance in a Northern American Prairie watershed. System Dynamics is employed as an effective methodology to organize and integrate existing information available on climate change scenarios, watershed hydrologic processes, reservoir operation and water resource assessment system. The second version of the Canadian Centre for Climate Modelling and Analysis Coupled Global Climate Model is selected to generate the climate change scenarios with daily climatic data series for hydrologic modeling. Watershed-based hydrologic and reservoir water dynamics modeling focuses on dynamic processes of both streamflow generation driven by climatic conditions, and the reservoir water dynamics based on reservoir operation rules. The reliability measure describes the effectiveness of present reservoir operation rules to meet various demands which are assumed to remain constant for the next 100 years in order to focus the study on the understanding of the structure and the behaviour of the water supply. Simulation results demonstrate that future climate variation and change may bring more high-peak-streamflow occurrences and more abundant water resources. Current reservoir operation rules can provide a high reliability in drought protection and flood control.     

A Neural Networks Approach for Deriving Irrigation Reservoir Operating Rules

A neural networks approach is applied to the derivation of the operating rules of an irrigation supply reservoir. Operating rules are determined as a two step process: first, a dynamic programming technique, which determines the optimal releases byminimizing the sum of squared deficits, assumed as objective function, subject to various constraints is applied. Then, theresulting releases from the reservoir are expressed as a functionof significant variables by neural networks. Neural networks aretrained on a long period, including severe drought events, andthe operation rules so determined are validated on a differentshorter period. The behaviour of different operating rules is assessed by simulating reservoir operation and by computing several performance indices of the reservoir and crop yield through a soil water balance model. Results show that operating rules based on an optimization with constraints resembling real system operation criteria lead to a good performance both in normal and in drought periods, reducing maximum deficits and water spills.     

Optimal and Adaptive Operation of a Hydropower System with Unit Commitment and Water Quality Constraints

Management of water resources has become more complex in recent years as a result of changing attitudes towards sustainability and the attribution of greater attention to environmental issues, especially under a scenario of water scarcity risk introduced by climate changes and anthropogenic pressures. This study addresses the optimal short-term operation of a multi-purpose hydropower system under an environment where objectives are conflicting. New optimization models using mixed integer nonlinear programming (MINLP) with binary variables adopted for incorporating unit commitment constraints and adaptive real-time operations are developed and applied to a real life hydropower reservoir in Brazil, utilizing evolutionary algorithms. These formulations address water quality concerns downstream of the reservoir and optimal operations for power generation in an integrated manner and deal with uncertain future flows due to climate change. Results obtained using genetic algorithm (GA) solvers were superior to gradient based methods, converging to superior optimal solutions especially due to computational intractability problems associated with combinatorial domain of integer variables in the unit commitment formulation. The adaptive operation formulation in conjunction with the solution of turbine unit commitment problem yielded more reliable solutions, reducing forecasting uncertainty and providing more flexible operational rules.     

Predictive Temporal Data-Mining Approach for Evolving Knowledge Based Reservoir Operation Rules

The persistent problem in reservoir operation is that the derived optimal releases fail to incorporate the decision maker or reservoir operators’ knowledge into reservoir operation models. The reservoir operators’ knowledge is specific to that particular reservoir and incorporating such an experienced knowledge will help to derive field reality based operation rules. The available historical reservoir operation databases are the representative samples of reservoir operators’ knowledge or experience. Thus, an attempt has been made that deals with the development of a methodological framework to recover or explore the historical reservoir operation database to derive the reservoir operators’ knowledge as operational rules. The developed methodological framework utilizes the strength and capability of recently developed predictive datamining algorithms to recover the knowledge from large historical database. Predictive data-mining algorithms such as a) classifier: Artificial Neural Network (ANN), and b) regression: Support Vector Regression (SVR) have been used for single reservoir operation data-mining (SROD) modelling framework to explore the temporal dependence between different variables of reservoir operation. The rules of operation or knowledge learned from the training database have been used as guiding rules for predicting the future reservoir operators’ decision on operating the reservoir for the given condition on the inflow, initial storage, and demand requirements. The developed SROD model was found to be efficient in exploring the hidden relationships that exist in a single reservoir system.     

Behaviour and Performance of a Water Resource System in Québec (Canada) Under Adapted Operating Policies in a Climate Change Context

The behaviour of the water resource system of the Peribonka River (Quebec, Canada) exploited for hydropower is evaluated under various hydrological regimes, using different climate change scenarios. The hydrological regime of the recent past and the regimes of 30 climate projections are considered. The potential hydrological regimes are simulated for climate projections from five general circulation models (GCM) for two greenhouse gas emission scenarios and three temporal horizons (2020, 2050 and 2080). For each hydrological regime, weekly reservoir operating rules are calculated with a dynamic and stochastic optimization model. Simulations of the water resource system with adapted operating rules in these climate change contexts are compared with the management of the water resource system at the control period (1961–1990). For the majority of climate projections, the analysis of simulations in the context of climate change shows an increase in hydropower and in annual unproductive spills. These increases reach 22% and 300%, respectively, compared to the control period. Also, the reliability of a reservoir is compromised for half of the climate projections, with annual probabilities reaching above the maximum operating levels, up to 0.3%, whereas these probabilities were null for the control period. Despite the rise in production, the annual efficiency of the power plants would fluctuate between −5 to +8%, depending on the power plant, the climate projection and the horizon.     

The influence of climate variations on an irrigation water resources system performance strategy

The influence of climate change on the performance strategy of an irrigation water resources system (WRS) containing a reservoir cascade is discussed as a decision-making problem under uncertainty. There are: (1) a set of climate change scenarios and (2) a set of river runoff sequences and a set of irrigation demand sequences with various statistical characteristics (sets (2) correspond to every scenario (1)). The function of transfer from the scenarios to the sequences is defined as certain subjective probabilities. The probabilities reflect the degree of the expert confidence in the plausibility of hydrology and moistening hypothesis. There is an index showing the degree of departure of WRS performance from the normal and from the worst values. The proposed technique allows us to (a) generate hydrology and water demand scenarios; (b) calculate the subjective probabilities; (c) compute the irrigation rates as a function of precipitation, radiation balance, etc., and then to compute of irrigation demand schedules; (d) to simulate the WRS. The algorithms of water resources distribution between the users and of WRS operation with stochastic water demands were implemented in a simulation model. The Terek river basin (North Caucasus, Russia) was taken for sample computations.     

基于模糊集对分析的灌区水库旱限水位及供水策略优化研究

随着经济社会发展和气候条件改变,我国区域干旱问题日益突出,水库在抗旱减灾过程中的作用越来越凸显,旱限水位的提出与应用对有效发挥大中型水库在抗旱中的调控作用、缓解流域旱情具有重要意义。针对旱限水位研究现状,本文以史河灌区梅山水库为例,首先考虑城乡供水、农业灌溉、水力发电和生态环境等诸多因素,构建基于模糊集对分析法的水库灌区水资源系统运行效果综合评价模型,并以水资源系统综合运行等级最优为目标,以分期分级旱限水位、灌溉限供措施(基于水库来水特征和作物生长特性)为优化变量,运用免疫遗传算法进行系统优化求解,最后对比有无旱限水位下水库水资源系统综合运行效果,并分析了不同控制方式下水库供水变化和灌区作物因旱减产风险。结果显示：该优化方法相较于以水库供水经济效益为目标旱限水位确定方法,多项年均指标都有较大改善,其中梅山水库经济效益提高了46万元,充反调节水库水量增加了555万m³,而水库弃水量减少了566万m³;史河灌区塘坝供水量增加了253万m³,而作物减产率下降了3.32%,同时作物因旱减产风险降低了3.15%。可见,运用系统综合...     

Optimal Hydropower Generation Under Climate Change Conditions for a Northern Water Resources System

This paper examines climate change impacts on the water resources system of the Manicouagan River (Québec, Canada). The objective is to evaluate the performance of existing infrastructures under future climate projections and the associated uncertainties. The main purpose of the water resources system is hydropower production. A reservoir optimization algorithm, Sampling Stochastic Dynamic Programming (SSDP), was used to derive weekly operating decisions for the existing system subject to reservoir inflows reflecting future climate, for optimum hydropower production. These projections are simulations from the SWAT hydrologic model for climate change scenarios for the period from 2010 to 2099. Results show that the climate change will alter the hydrological regime of the study area: earlier timing of the spring flood, reduced spring peak flow, and increased annual inflows volume in the future compared to the historical climate. The SSDP optimization algorithm adapted the operating policy to the future hydrological regime by adjusting water reservoir levels in the winter and spring, and increasing the release through turbines, which in the end increased power generation. However, there could be more unproductive spills for some power plants, which would decrease the overall efficiency of the existing water resources system.     

水库适应性调度初探

变化环境下的水库适应性调度是国际水库调度领域的前沿课题。针对"水库适应性调度",在实时调度方面需重点研究变化环境下的水文预报技术,提高预报精度;在规划和调度规则方面需构建全新的调度规则再编制技术,涉及水文频率分析、非一致性条件下评价指标体系以及水库调度规则的自适应跟踪控制等。为此,从水文时变预报预测、调度指标的动态评价、调度规则的跟踪控制以及柔性调度规则等4个方面提出研究思路与方法,为水库适应性调度理论和方法提供参考。