Optimal Development of Agricultural Sectors in the Basin Based on Economic Efficiency and Social Equality

The limitation of freshwater resources and the growing demand for water, make the issue of water resource development planning and water allocation among stakeholders even more important. Ideally, water allocation should be economically efficient and socially equitable. In this study, a water allocation model is presented in an integrated framework that considers the interaction of water supply and demand according to economic and social factors. To achieve this, a reliability-based multi-objective optimization - simulation approach has been employed. The objective functions of the problem are: 1) maximizing GDP from agricultural sectors and 2) maximizing social equality in different provinces of the basin (measured using the Williamson coefficient). The fair development and allocation among the shared provinces in the basin can reduce conflicts in the region. Karkheh basin has been considered as a case study and decision variables of the problem are area under cultivation of agricultural development sectors in different provinces. The results show that, without harming the income of the agricultural sector, the spatial distribution of development projects can be done in such a way that equality (according to income level and the number of people working in each province) is achieved. One of the solutions of Pareto front compared to previous studies shows that, in addition to an increase of about 12% of the objective function 1 (GDP), the value of the objective function 2 (Williamson coefficient) decreased from 1.19 to 0.98. This indicates a decrease in income inequality among the provinces of the basin.

     

改进的风险决策及NSGA-Ⅱ方法在马莲河流域水资源综合管理中的应用

将马莲河流域系统概化为5个水资源分区,采用指标分析法进行2020和2030年供需水量预测和平衡分析。利用改进的决策树法进行风险分析,将专家咨询概率法引入到决策树敏感性分析中,最终得到2020、2030年的推荐水资源供需分配方案。然后,利用系统分析理论和优化技术建立了流域的大系统、多目标水资源优化配置模型,并采用优化的NSGA-Ⅱ方法进行求解,得到流域2030年推荐水资源配置方案下的最佳分配方案。最后,根据最佳分配方案和预测的流域需水量,进行了流域的水资源平衡分析,通过平衡分析的结果进行流域的综合管理研究。最佳配置方案实现了流域内水资源的最优分配,使宝贵、有限的水资源产生最大的社会、经济及环境效益,为流域经济、能源产业的快速发展提供水资源保障。     

Multi-Objective Surface Water Resource Management Considering Conflict Resolution and Utility Function Optimization

In the present research, a multi-objective model is developed for surface water resource management in the river basin area which is connected to the lake. This model considers different components of sustainable water resource management including economic, social and environmental aspects, and simultaneously tries to resolve conflicts between different stakeholders by means of non-symmetric Nash bargaining, which is linked to the multi-objective optimization method. This study proposes a new methodology to improve Nash Conflict Resolution through finding the optimum degree of the utility function. The proposed model is examined in the Zarrineh River basin in Iran. The results show that the amount of available resources or volume of reservoirs play a significant role in determining the optimal degree of the utility function and efficiency of the proposed method in such a way that the higher amount of resources or the larger reservoirs will result in the higher optimal degree of the utility function. In the proposed multi-objective model, two different amounts of surface water inflow are considered. The first assumed amount is the long-term average flow rate and the second one is equal to 80% of the first mode, which is reduced based on the estimated impacts of climate changes. This multi-objective allocation model could supply 100 and 97.5% of the environmental demand of Lake Urmia in the first and second situations, respectively.     

Indicators and a Neuro-Fuzzy Based Model for the Evaluation of Water Supply Sustainability

In the field of water management, the evaluation of sustainability has no universal methodological approach nor any consensus regarding the definitions of sustainability or sustainable development. There is an essential need therefore for a precise definition of sustainability in different water management fields. This paper deals with the sustainability of one part of urban water management, namely a water supply. A precise definition of a sustainable water supply system is given, together with a methodological framework that quantifies the degree of water supply sustainability. The proposed framework relies on the proposed quasi-strong sustainability concept, its components (dimensions), their particular relationships and corresponding indicators representing individual processes in the water supply system and utility. The processing of indicators is performed through a joint fuzzy logic/neural network (ANFIS) model. In order to evaluate each sustainability component, separate ANFIS models were created whose results were aggregated into a single result (sustainability index). According to the presented framework, sustainability is evaluated for 17 public water supply systems/utilities in Croatia.     

Participatory Optimization Scenario for Water Resources Management: A Case from Jordan

Participatory optimization scenario process was developed for water resources management of the Zarqa River Basin in Jordan. The basin was selected to represent a case study of semi-arid area in the Mediterranean because of its entire range of prototypical water management problems. The nature of the institutional framework, the severity of the water related problems and concentration of population in the basin have required the need for stakeholders’ involvement in the optimization process. The paper demonstrates a Water Resources Model (WRM) consisting of integrated cascade of modules, embedded in a framework of a participatory approach in water resources optimization. The system includes baseline scenario, identification of constrains and instruments, the optimization scenario and analysis of results. In a participatory approach, stakeholders identified the optimization criteria (constraints) and the management interventions (instruments). Constraints were set to securing high supply/demand ratio of 0.98 and improving reliability of supply to 75 %, while specific eight instruments were suggested and manipulated by the model to achieve the above criteria. The results of the WRM optimization scenario showed that the specified constraints were met so that the supply/demand ratio increased from 0.90 to 0.996 and the reliability of supply improved from 58 % to 84 %. The benefit/cost ratio, water shortfall, and the economic efficiency had responded effectively. The model proved its efficiency in using the full featured basin characteristics towards baseline and optimization scenarios with the support of stakeholders in simulating the basin behavior over time using the model parameters.     

Global Water Demand and Supply Projections

Abstract

This paper presents a modeling approach for projections of water demand and supply for domestic, industrial, livestock, and irrigation at the basin or country level in a global scope. Particular emphasis is put on simulating water availability for crops taking into account total renewable water, non-irrigation water demand, water supply infrastructure, and economic and environmental policies at the basin or country level. This paper focuses on concepts and methodology involved in the modeling exercise. Data assessment and results are presented in a companion paper (Rosegrant and Cai, 2002).     

Multi-Objective Optimization of Integrated Surface and Groundwater Resources Under the Clean Development Mechanism

Acquiring sustainable water resources for water-based development of countries is the experts? concern in this field, who seek to follow the clean development mechanism (CDM) regulations and overcome water crisis through integrated water resources management (IWRM). The Great Karun River basin is one of the major basins in the Middle East. This basin, containing six of the largest reservoir dams with a cumulative power plant capacity of more than 10,500 MW generates about 93% of hydropower of Iran. The water balance of the aquifer in the study area was simulated using MODFLOW model while water resources and surface water reserves were simulated by the water evaluation and planning (WEAP) model. A separate simulation was performed with each of two models and the results of two models were coupled using a link file. The multi-objective function optimization process including the maximized supply of demands and hydropower and the minimized aquifer drawdown was completed using non-dominated sorting genetic algorithm (NSGA-II). All effective system components, such as inter-basin water transfer, integrated use of water resources, variation of irrigation network efficiencies, and the effect of water shortage were studied and analyzed under the targeted scenarios. Finally, the best scenario, which was capable to supply the future needs until time horizon of 2040 was planned for the basin considering minimization of aquifer drawdown and optimal generation of hydropower resulting in a maximum decrease in emission of greenhouse gases.

     

A Multi-Objective Risk-Based Game Theoretic Approach to Reservoir Operation Policy in Potential Future Drought Condition

In this paper, by using the concept of Conditional Value at Risk (CVaR), a Leader-Follower game (LFG) based multi-objective optimization model is developed to determine the optimum 12-month operation policy of a reservoir in potential future dry periods. The minimization of CVaRs of storage loss and agricultural and environmental deficits along with maximization of planned allocation to agricultural sector are considered as leader’s objectives, while the followers try to maximize their share of water rights using Nash bargaining (NB) method. This framework is then used to model the operation policy of Dorudzan basin in Fars province, southwestern Iran. Water demand and daily climate data in the period of 2003 to 2015 for this basin, as well as future projections from fifteen IPCC-AR4 global circulation models (GCMs) for 2018–2030 under A2, B1 and A1B emission scenarios are considered to evaluate future dam operation policies. Future projections are downscaled using the LARS-WG model, which then feeds the HMETS watershed model to simulate the corresponding reservoir inflow time-series. Thereafter, three-hundred 12-month rainfall, evaporation and inflow time series with least inflow volume are used as input for the optimization model, which is solved using NSGA-II and GA algorithms. The results show while the model can determine the operation policy that keeps the associated risks in the acceptable range, it can satisfy the followers demands with respect to the available resources. The results also show that the agricultural sector of the study area can be hugely affected by potential future droughts.

     

Optimization and Evaluation of Environmental Operations for Three Gorges Reservoir

This study proposed a multi-objective environmental reservoir operation methodology (MOEROM) and applied the new methodology to the Three Gorges Reservoir (TGR) in China. The MOEROM directly incorporated the statistical water quality models in the objective functions to reflect the response relationships between reservoir operations and water quality in three tributaries (the Xiangxi River, the Daning River and the Xiaojing River). A real-coded genetic algorithm with self-adaptive crossover operators was employed to solve the environmental reservoir operation model. And four different MCDA approaches (AHP, TOPSIS, Fuzzy AHP, and Fuzzy TOPSIS) were proposed to evaluate the optimal operational schemes. The results showed that TOPSIS and AHP were more suitable for selecting the schemes than the others because of the larger fluctuations. To demonstrate the advantage of MOEROM, the environmental operation schemes were compared with traditional and practical operation schemes in 2013. The advantages of the environmental operation scheme over the others were that it would help reach a compromise between economic benefit, flood control and water quality improvement. In the future, more water quality indices, uncertainties in water environmental information, and more efficient algorithms would be further considered and researched.     

Multi-Objective Simulation-Optimization Model for Long-term Reservoir Operation using Piecewise Linear Hedging Rule

An efficiently parameterized and appropriately structured piecewise linear hedging rule is formulated and included within a multi-objective simulation-optimization (S-O) framework that seeks to obtain Pareto-optimal solutions for the long-term hedged operation of a single water supply reservoir. Two conflicting objectives, namely, “minimize the total shortage ratio” and “minimize the maximum shortage” are considered in the S-O framework, while explicit specification of constraints is avoided in the optimization module. Evolutionary search based non-dominated sorting genetic algorithm is used as the driver, which is linked to the simulation engine that invokes the piecewise linear hedging rule within the S-O framework. Preconditioning of the multi-objective stochastic search of the time-varying piecewise linear hedging model is effected by feeding initial feasible solutions sampled from the Pareto-optimal front of a simple constant hedging parameter model, which has resulted in significant improvement of the Pareto-optimality and the computational efficiency.

     

Economic Analysis of Environmental Issues Regarding Seawater Desalination

Abstract

Desalination of seawater is becoming an important source of drinking water in many cities and towns, where the demand for water continues to increase, while the supply of fresh water is limited. We examine the economic aspects of several environmental issues that arise during consideration of desalination projects. We present a conceptual framework that demonstrates the differences between public and private objectives, and we use that framework to describe policies that can encourage firms and consumers to consider environmental impacts. Public policies can be implemented also to stimulate technological developments that will reduce the environmental impacts of desalination. We also examine the potential growth inducement effects of seawater desalination by describing the likely impacts of desalination on both the demand and supply components of the market for municipal water in a coastal area.     

中国南方季节性缺水地区水资源合理配置研究

本文运用大系统多目标分析原理，建立了一种以水资源合理利用为目标，综合考虑防洪、供水、河道生态等约束的水资源合理配置模型，并运用大系统“分解协调”原理提出了相应算法。该模型为反映需水部门子系统的公平性，引入了经济学中的基尼系数，针对东江流域枯水期季节性缺水以及由于河道咸潮活动频繁、水体污染严重而造成缺水明显的特点，对该流域水资源进行了合理的配置，得出了满意的水资源合理配置方案，证实了本文模型的可行性。     

A Fair Approach for Multi-Objective Water Resources Allocation

Due to the effect of climate change, rapid population growth and widespread water pollution, fresh water becomes an increasingly scarce natural resource. Optimal allocation of water resources is one of the most effective resolutions to deal with rising water demand and insufficient freshwater resources. This study proposes a fair approach for water resources allocation by employing the Sperner’s lemma to solve the conflicts of different objectives and those of competing regions. A multi-objective optimal allocation model is firstly formulated to generate the Pareto frontier surface, which maximizes the economic interest while minimizing the amount of organic pollutants. Subsequently, the approach searches for acceptable allocation schemes over the Pareto frontier surfaces through the total water quantity and envy-free constraints. The proposed model is applied to the middle and lower reaches of Hanjiang river basin in China. Results indicate that water allocation between multi-region can achieve Nash equilibrium by using the water conflict resolution method to select fair water allocation schemes, in which each region obtains its preferred water quantity. The proposed approach is proved effective for water resources management in the case study and demonstrates the potential for effective application in other basins.

     

Evaluating the Performance of Agricultural Water Distribution Systems Using FIS,ANN and ANFIS Intelligent Models

Increasing water use efficiency in the agricultural sector requires the use of appropriate methods for intelligent performance evaluation of surface water distribution systems in agriculture. Therefore, in this study a systematic approach was developed for operational performance appraisal of the agricultural water distribution systems. For this purpose, Fuzzy Inference System (FIS), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to evaluate the technical performance of irrigation network, considering the uncertainties in the water exploitation process. The performance of the developed models was studied on the Roodasht irrigation canal, located in central Iran, which suffers from severe fluctuations in the inflow, by evaluating the adequacy, efficiency, and equity of surface water distribution. Hydraulic simulation of water distribution system, as well as providing the information required for training and validation of the intelligent models, were performed using the HEC-RAS model. The results showed that compared to the FIS model, ANN and ANFIS models similarly predicted the model outputs with lower errors at almost the same level. The adequacy, efficiency, and equity indicators were predicted by ANFIS model with MAPE of 0.16, 0.01 and 0.23, respectively. Also, FIS model was only able to predict the efficiency and could not predict the adequacy and equity with appropriate performance. The findings of this study reveal that since the ANFIS model uses both FIS and ANN models in its structure, it considers the model uncertainty reliably, and it can be used to evaluate the performance of agricultural water systems.

     

近52年和林格尔气温和降水量变化

针对高强度水资源开发利用条件下流域水循环研究存在的问题,从认知、试验、理论、方法四个层次探讨了适应水资源开发利用条件的流域水循环的研究方法,构建了一个以水资源配置模型与水循环模型耦合为核心,以流域供需水分析和流域生态、环境、经济综合评价为响应的多目标调控模型。利用该模型,对黄河流域水资源进行了合理配置计算。结果表明,现状条件下黄河流域缺水严重;而在未来情境下缺水将更加严重,大力节水能够较大程度缓解缺水状况。     

基于可持续发展的区域水资源优化配置研究

运用可持续发展的水资源优化配置理论,以区域经济发展与水环境保护相协调为目标,建立地下水开采漏斗区水资源多目标优化配置模型。采用多目标遗传算法对漏斗区水资源进行合理配置。优化配置结果表明:基本解决了漏斗区供需矛盾,缓解了地下水的开采压力。     

Modelling Intelligent Water Resources Allocation for Multi-users

This study proposes intelligent water resources allocation strategies for multiple users through hybrid artificial intelligence techniques implemented for reservoir operation optimization and water shortage rate estimation. A two-fold scheme is developed for (1) knowledge acquisition through searching input–output patterns of optimal reservoir operation by optimization methods and (2) the inference system through mapping the current input pattern to estimate the water shortage rate by artificial neural networks (ANNs). The Shihmen Reservoir in northern Taiwan is the study case. We first design nine possible water demand conditions by investigating the changes in historical water supply. With the nine designed conditions and 44-year historical 10-day reservoir inflow data collected during the growth season (3 months) of the first paddy crop, we first conduct the optimization search of reservoir operation by using the non-dominated sorting genetic algorithm-II (NSGA-II) in consideration of agricultural and public water demands simultaneously. The simulation method is used as a comparative model to the NSGA-II. Results demonstrate that the NSGA-II can suitably search the optimal water allocation series and obtain much lower seasonal water shortage rates than those of the simulation method. Then seasonal water shortage rates in response to future water demands for both sectors are estimated by using the adaptive network fuzzy inference system (ANFIS). The back-propagation neural network (BPNN) is adopted as a comparative model to the ANFIS. During model construction, future water demands, predicted monthly inflows (or seasonal inflow) of the reservoir in the next coming quarter and historical initial reservoir storages configure the input patterns while the optimal seasonal water shortage rates obtained from the NSGA-II serve as output targets (training targets) for both neural networks. Results indicate that the ANFIS and the BPNN models produce almost equally good performance in estimating water shortage rates, yet the ANFIS model produces even better stability. The reliability of the proposed scheme is further examined by scenario analysis. The scenario analysis indicates that an increase in public water demand or a decrease in agricultural water demand would bring more impacts of water supply on agricultural sectors than public sectors. Similarly, a bigger decrease in inflow amount would obviously bring more influence on agricultural sectors than public one. Consequently, given predicted inflow, decision makers can pre-experience the possible outcomes in response to competing water demands through the estimation models in order to determine adequate water supply as well as preparedness measures, if needed, for drought mitigation.     

Approximation of Metro Water District Basin Using Parallel Computing of Emulator Based Spatial Optimization (PCESO)

Metro Water District (MWD) is an agency that administers water distribution in a large geographic region. It targets for existing conditions with future projections of water resources for conservation, supply, and usage. Hence, it is required to show proper water resources management for MWD. Where the river basin profiles are projected to provide the water resources management with potential issues for MWD. Here, Upper Chattahoochee River (UCR) basin of the Metropolitan North Georgia Water Planning District (MNGWPD) selected for the study area. UCR is one of the largest river basins in the MNGWPD and it provides drinking and primary receiving water for nearly 3.5 million people of Atlanta Metro Region. In this study, Parallel Computing of Emulator based Spatial Optimization (PCESO) framework developed for spatial optimization of large complex watersheds. The proposed framework optimizes the hydrological model by parallel computing, emulator fit, sampling design, and spatial optimization. The results showed that 1) the computational time required for spatial optimization was significantly reduced by 50%, 2) goodness-of-fit reached its threshold limit in all stations inclusive in reservoir containing stations, 3) the water balance components and the optimized parameter values with sensitivity index provided the physical phenomena of the study area and showed the approximate hydrological processes in MWD. Further, this proposed work incorporates into future climate data can provide an accurate hydrological analysis with water allocation issues like water use, demand, conservation, and supply for MWD and it helps to identify the water-related disasters floods and droughts.

     

Ensemble Based Forecasting and Optimization Framework to Optimize Releases from Water Supply Reservoirs for Flood Control

Water supply reservoir management is based on long-term management policies which depend on customer demands and seasonal hydrologic changes. However, increasing frequency and intensity of precipitation events is necessitating the short-term management of such reservoirs to reduce downstream flooding. Operational management of reservoirs at hourly/daily timescales is challenging due to the uncertainty associated with the inflow forecasts and the volumes in the reservoir. We present an ensemble-based streamflow prediction and optimization framework consisting of a regional scale hydrologic model forced with ensemble precipitation inputs to obtain probabilistic inflows to the reservoir. A multi-objective dynamic programming model was used to obtain optimized release strategies accounting for the inflow uncertainties. The proposed framework was evaluated at a water supply reservoir in the Hackensack River basin in New Jersey during Hurricanes Irene and Sandy. Hurricane Irene resulted in the overtopping of the dam despite releases made in anticipation of the event and resulted in severe downstream flooding. Hurricane Sandy was characterized by low rainfall, however, raised significant concerns of flooding given the nature of the event. The improvement in NSE for the Hurricane Irene inflows from 0.5 to 0.76 and reduction of the spread of PBIAS with decreasing lead times resulted in improvements in the forecast informed releases. This study provides perspectives on the benefits of the proposed forecasting and optimization framework in reducing the decision making burden on the operator by providing the uncertainties associated with the inflows, releases and the water levels in the reservoir.

     

Multi-Objective Firefly Integration with the K-Nearest Neighbor to Reduce Simulation Model Calls to Accelerate the Optimal Operation of Multi-Objective Reservoirs

Reservoirs are used as one of the surface water sources for different and often conflicting water supply purposes. Given the complex management policies governing a basin, it is essential to simultaneously consider different goals and cope with the associated trade-off in water resources management. This purpose requires coupling a multi-objective optimization algorithm with a reservoir simulation model, which this approach increases required computational efforts. Various simulation–optimization approaches have been developed and used for solving the related problems. However, they often have complicated methods and certain limitations in real-world applications. In this study, a new multi-objective firefly algorithm—K nearest neighbor (MOFA-KNN) hybrid algorithm is developed which is time-efficient and is not as complicated as previous approaches. The proposed algorithm was evaluated for both benchmark and real problems. The results of the benchmark problem showed that the execution time of the MOFA-KNN hybrid algorithm was up to 99.98% less than that of the multi-objective firefly algorithm (MOFA). In the real problem, the MOFA-KNN algorithm was linked to the 2D hydrodynamic and water quality model, CE-QUAL-W2, to test the developed framework for reservoir operation. The Aidoghmoush reservoir as a case study investigated to minimize the total released dissolved solids (TDS) and the water temperature difference between the inflow and the outflow. The results demonstrated that the MOFA-KNN algorithm significantly reduced the simulation–optimization execution time (>?660 times compared with MOFA). The minimum released TDS from the reservoir was 13.6 mg /l and the minimum temperature difference was 0.005 °C.