Simulation-Optimization Modeling for Conjunctive Use Management under Hydrological Uncertainty

The canal water supply, which is the only source of irrigation, in the rice-dominated cropping system of the Hirakud canal command (eastern India) is able to meet only 54 % of the irrigation demand at 90 % probability of exceedance. Hence, considering groundwater as the supplemental source of irrigation, conjunctive use management study by combined simulation-optimization modelling was undertaken in order to predict the maximum permissible groundwater pumpage from the command area. Further, optimal land and water resources allocation model was developed to determine the optimal cropping pattern for maximizing net annual return. The modelling results suggested that 2.0 and 2.3 million m³ of groundwater can be pumped from the bottom aquifer during monsoon and non-monsoon seasons, respectively, at 90 % probability of exceedance of rainfall and canal water availability (PERC). Optimal cropping patterns and pumping strategies can lead to about 51.3–12.5 % increase in net annual return from the area at 10–90 % PERC. The sensitivity analysis of the model indicates that the variation in the market price of crops has very high influence on the optimal solution followed by the cost of cultivation and cultivable area. Finally, different future scenarios of land and water use were formulated for the command area. The adoption of optimal cropping patterns and optimal pumping strategies is strongly recommended for sustainable management of available land and water resources of the canal command under hydrological uncertainties.     

Simulation-Optimization Modeling of Conjunctive Use of Surface Water and Groundwater

Water resources management in semiarid regions with low precipitation and high potential of evapotranspiration is a great challenge for managers and decision makers. In those regions, both sources of water should be managed conjunctively so as to minimize shortages of water in dry seasons. In conjunctive use, the difficulty increases as one must represent the response of both systems interactions, and develop management strategies that simultaneously address surface water and aquifer regulation. This paper focuses on the simulation-optimization for conjunctive use of surface water and groundwater on a basin-wide scale, the Najafabad plain in west-central Iran. A trained artificial neural network model is developed as a simulator of surface water and groundwater interaction while a genetic algorithm is developed as the optimization model. The main goal of the simulation-optimization model is to minimize shortages in meeting irrigation demands for three irrigation systems subject to constraints on the control of cumulative drawdown of the underlying water table and maximum capacity of surface irrigation systems. To achieve the main goal, three scenarios are presented. Results of the proposed model demonstrate the importance of the conjunctive use approach for planning the management of water resources in semiarid regions.     

Performance Assessment of a Coupled Particle Swarm Optimization and Network Flow Programming Model for Optimum Water Allocation

Water resources allocation problems are mainly categorized in two classes of simulation and optimization. In most cases, optimization problems due to the number of variables, constraints and nonlinear feasible search space are known as a challenging subject in the literature. In this research, by coupling particle swarm optimization (PSO) algorithm and a network flow programming (NFP) based river basin simulation model, a PSO-NFP hybrid structure is constructed for optimum water allocation planning. In the PSO-NFP model, the NFP core roles as the fast inner simulation engine for finding optimum values for a large number of water discharges in the network links (rivers and canals) and nodes (reservoirs and demands) while the heuristic PSO algorithm forms the outer optimization cover to search for the optimum values of reservoirs capacities and their storage priorities. In order to assess the performance of the PSO-NFP model, three hypothetical test problems are defined, and their equivalent nonlinear mathematical programs are developed in LINGO and the results are compared. Finally, the PSO-NFP model is applied in solving a real river basin water allocation problem. Results indicate that the applied method of coupling PSO and NFP has an efficient ability for handling river basin-scale water resources optimization problems.     

Optimal Allocation of Agricultural Water Resources Based on Virtual Water Subdivision in Shiyang River Basin

Without subdividing into blue and green virtual water, the virtual crop water is currently used in the allocation of water resources based on virtual water strategy. In order to improve agricultural water use efficiency and the proportion of green water utilization, a multi-objective optimal allocation model for agricultural water resources is developed in this study. The model is based on the subdivision of virtual water into blue and green virtual water, subject to three objectives of the maximum net benefit from agriculture, the minimum fairness difference in the utilization of water, and the maximum proportion of green water utilization. Taking Shiyang River basin as an example, agricultural water resources are optimized through regional virtual water trade in the basin. Results show that compared with the situation in the year 2007, the net benefit of agriculture, the fairness difference in the utilization of water, and the proportion of green water utilization are optimized. At the same time, the planting ratio of food crops, such as corn, reduces, while the planting ratio of cash crops, such as cotton, vegetables, and fruits, increases. Through regional virtual water strategy in the basin, with the crops of different districts having comparative advantages, the proportion of green water utilization and the blue water use efficiency are improved. The study provides a scientific basis to solve the water shortage problem in the basin.     

应用分布式水文模型优化黑龙港及运东平原农田灌溉制度Ⅱ：水分生产函数和优化灌溉制度

黑龙港及运东平原是河北省的主要农业区,同时也是华北平原的少雨中心,探讨适用于该地区冬小麦和夏玉米的水分生产函数类型,并应用于制定优化的灌溉制度,对于该区域节水农业的建设具有重要的现实意义。本文以经过参数率定与模拟验证的土壤水评价工具(SWAT)为手段,设置了1种充分灌溉处理和9种非充分灌溉情形,拟合得到冬小麦和夏玉米的水分生产函数,在此基础上,依据水分敏感系数(或指数)确定出灌溉的关键生育期,以不考虑氮磷胁迫的历史灌溉情景为基本情景,设置了3种优化灌溉方案。结果表明:在所确定的冬小麦和夏玉米灌溉的关键生育期,采用优化后的灌溉量,与基本情景相比,在保证冬小麦-夏玉米种植制度下的作物基本稳产(产量平均增加2.54%)的前提下,平均节省灌溉量23.55%,水分利用效率平均提高6.29%。总之,模拟得到的优化的灌溉制度对于该区域冬小麦-夏玉米轮作体系下的农田节水灌溉管理具有一定的参考价值。     

山西省主要农作物优化灌溉制度的制定及其应用

文中通过数学模型，采用优化的方法，研究全省62个典型县主要农作物在不同水文年产量与灌水量之间的关系，制定不同灌溉供水条件下主要农作物的优化灌溉制度。为便于优化灌溉制度的推广应用，文中对之进行了详细的分析，并介绍了优化灌溉制度的应用及应用范围和基本方法。     

Crop Calendar Adjustment Study for Sathanur Irrigation System in India Using Genetic Algorithm

The multiple objectives and interests of various stack holders’ in reservoir operation bring forward many solutions to the problem. Possibly due to this complexity, in practice, the operating rules to be derived not only from heuristic approaches such as rules of thumb, rule curve, operator experience and engineering judgments but also through well defined mathematical models. An attempt is made to study the possibility of changing the crop (paddy crop instead of groundnut) and crop starting period for Sathanur irrigation system, India. This system is considered as one of the water-deficit irrigation system where the water is collected in the reservoir during north-east monsoon (Oct–Dec) and crops are cultivated after the rainfall season. Even though the first priority is to supply drinking water to Tiruvanamalai town and nearby villages, the quantity required for drinking water is very meagre when compared to irrigation demands. Genetic algorithm (GA) has been used to find the optimal crop-starting period that can give maximum performance and net benefit from the system. The results of the model study indicate that starting the cultivation of paddy crop between 1st and 10th of October every year provides maximum net benefit than starting the cultivation between 11th and 20th of December as practiced now.     

Simulation-Optimization Modeling for Sustainable Groundwater Development: A Moroccan Coastal Aquifer Case Study

A transient simulation model characterizing groundwater flow in the coastal aquifer of Rhis-Nekor was constructed and calibrated. The flow model was then used in conjunction with a genetic algorithm based optimization model to explore the optimal pumping schemes that meet current and future water demands while minimizing the risks for several adverse environmental impacts, such as saltwater intrusion prevention, avoiding excessive drawdown, as well as controlling waterlogging and salinity problems. Modeling results demonstrate the importance of this combined simulation-optimization methodology for solving groundwater management problems associated with the Rhis-Nekor plain.     

Optimal Inter-Basin Water Allocation Using Crisp and Fuzzy Shapley Games

In recent years, uneven distribution of available water resources as well as increasing water demands and overexploiting the water resources have brought severe need for transferring water from basins having sufficient water to basins facing water shortages. Therefore, optimal allocation of shared water resources in water transfer projects, considering the utilities of different stakeholders, physical limitations of the system and socioeconomic criteria is an important task. In this paper, a new methodology based on crisp and fuzzy Shapley games is developed for optimal allocation of inter-basin water resources. In the proposed methodology, initial water allocations are obtained using an optimization model considering an equity criterion. In the second step, the stakeholders form crisp coalitions to increase the total net benefit of the system as well as their own benefits and a crisp Shapley Value game is used to reallocate the benefits produced in the crisp coalitions. Lastly, to provide maximum total net benefit, fuzzy coalitions are constituted and the participation rates of water users to fuzzy coalitions are optimized. Then, the total net benefit is reallocated to water users in a rational and equitable way using Fuzzy Shapley Value game. The effectiveness of this method is examined by applying it to a large scale case study of water transfer from the Karoon river basin in southern Iran to the Rafsanjan plain in central Iran.     

Coupled Quantity-Quality Simulation-Optimization Model for Conjunctive Surface-Groundwater Use

Many water resources optimization problems involve conflicting objectives which the main goal is to find a set of optimal solutions on, or near to, Pareto front. In this study a multi-objective water allocation model was developed for optimization of conjunctive use of surface water and groundwater resources to achieve sustainable supply of agricultural water. Here, the water resource allocation model is based on simulation-optimization (SO) modeling approach. Two surrogate models, namely an Artificial Neural Network model for groundwater level simulation and a Genetic Programming model for TDS concentration prediction were coupled with NSGA-II. The objective functions involved: 1) minimizing water shortage relative to the water demand, 2) minimizing the drawdown of groundwater level, and 3) minimizing the groundwater quality changes. According to the MSE and R² criteria, the results showed that the surrogate models for prediction of groundwater level and TDS concentration performed favorably in comparison to the measured values at the number of observation wells. In Najaf Abad plain case study, the average drawdown was limited to 0.18 m and the average TDS concentration also decreased from 1257 mg/lit to 1229 mg/lit under optimal conditions.     

A Hybrid Fuzzy-Based Multi-Objective PSO Algorithm for Conjunctive Water Use and Optimal Multi-Crop Pattern Planning

This paper focuses on extracting an optimal multi-crop pattern plan through multi-objective conjunctive surface-ground water use management. Minimizing shortages in meeting irrigation demands, maximizing groundwater resources sustainability and maximizing agricultural net benefits are the three main goals of the multi-objective optimization problem solved in this paper. A new robust fuzzy-based multi-objective PSO algorithm called f-MOPSO is adopted and modified to solve a three-objective real-world conjunctive use management problem presented in this paper after testing on standard test problems revealed f-MOPSO superiority as compared to the well-known multi-swarm vector evaluated PSO (VEPSO) algorithm. The f-MOPSO benefits from a well-organized Sugeno fuzzy inference system (SFIS) designed for handling multi-objective nature of the optimization problems. The unique performance of f-MOPSO is not only presenting the better final solutions, but also aggregating the capabilities for measurement of dominance and diversity of the solutions in one stage by one index named comprehensive dominance index, in contrast to a wide range of multi-objective algorithms that evaluate dominance and diversity in two separate stages resulting in excessive computational burden. The optimization model is carried out on a 10-year long-term simulation period, resulting in increasing irrigation efficiency i.e. decreasing water losses, decreasing water consumption per unit cultivated area and increasing water productivity compared to those similar criteria observed in actual operation in the study area. The wheat and rice crops were identified as the dominant crops, while the optimization model was the least interested to onion cultivation, assigning the least average cultivation area to this crop over the whole planning period.     

基于净效益最大的水资源优化配置模型与方法

本文分析了现有水资源配置理论的优缺点，提出了水资源净效益的新概念及内涵。从生态环境保护、水权转让、利益补偿、水价形成和集中控制等方面探讨了基于水资源净效益思想的水资源配置机制，确立了包括生存条件、承载能力、用水公平性和可持续性约束的水资源开发利用约束准则。构建了基于水资源净效益最大的水资源优化配置模型，该模型包括水资源配置方案生成模型和水资源配置合理性评价模型。经郑州市郑东新区龙子湖地区水资源优化配置实证分析，取得了实用性成果与结论。     

基于水分-品质响应关系的特色经济作物节水调质高效灌溉

摘要：传统的非充分灌溉理论主要基于水分生产函数考虑产量损失最小或总产量及效益最佳,较少考虑水分-品 质响应关系及其对节水效益的影响。针对此问题,本文归纳了节水调质高效灌溉的生理学、生物学和工程学基 础,探讨了水分-品质响应关系的研究进展与存在的问题。在此基础上,针对特色经济作物构建了基于水分-品质 响应关系的节水调质高效灌溉理论,提出了节水调质高效灌溉的实施思路,阐述了节水调质高效灌溉需要解决的 几个重要科学问题。以期能为充分挖掘作物生理节水潜力,大幅度提高作物水分利用效率和实现节水、丰产、优 质、高效的     

宁夏平原区分布式水循环模拟研究

根据平原区水循环的特点,建立了平原区分布式水循环模型,该模型以日为计算时段,根据行政区域、引排水渠道覆盖的灌域、土地利用类型以及作物种植结构划分水循环计算单元。模型以水平衡为基础,在任一计算单元沿垂直方向均可分为植被冠层、地表水、土壤浅层、土壤深层、潜水层和承压水层,按照水域、植被、裸地、农田、不透水域等不同土地利用类型计算蒸散发量,地表水模拟包括引水系统、排水系统、湖泊湿地和生活及工业用水系统。应用该模型详细模拟了宁夏平原区1991～2000年的水循环转化过程和供、用、耗、排水量关系,计算结果表明,若不加上黄河河道蒸发消耗水量,2000年平原区实际消耗黄河干流水量为33.53亿m3。     

Effectiveness and Efficiency of Scheduling Regional Water Resources Projects

The regional water resources planning contains a variety of proposed projects. Precisely estimating the regional system yield could be difficult. Incorrectly computed system yield will cause the scheduling strategy to be not correctly screened. This research imbedded a combined regional simulation-optimization model, namely Generalized Water Allocation Simulation Model (GWASIM), for evaluating system yield in binary dynamic programming (DP) analysis as a scheduling model to determine the optimal development strategy of the master plan of a regional water supply system. To achieve a complete scheduling analysis, this paper investigated: (1) applying GWASIM to effectively and efficiently determine the precise system yields of many different combinations of proposed projects while meeting design criteria of acceptable shortage; (2) solving a non-pure sequencing problem with a DP based scheduling model since some projects may not be necessarily developed, or even no feasible combination of proposed projects can meet the demand; (3) clarifying the amortized construction cost to be accounted in economic analysis if the economic life of a project exceeds the scheduling horizon. In addition, the computation efficiencies of scheduling analysis and system yield analysis, which are significantly improved with DP and simulation-optimization model respectively, are discussed in the paper. This study devised the master plan of the Jilong river system in northern Taiwan as an example. The optimal scheduling strategy of minimum developing cost was precisely determined.     

Optimal Reservoir Release Policy Considering Heterogeneity of Command Area by Elitist Genetic Algorithm

A number of models with conventional optimization techniques have been developed for optimization of reservoir water release policies. However these models are not able to consider the heterogeneity in the command area of the reservoir appropriately, due to non linear nature of the processes involved. The optimization model based on genetic algorithm (GA) can deal with the non linearity due to its inherent ability to consider complex simulation model as evaluation function for optimization. GA based models available in literature generally minimize the water deficits and do not optimize the total net benefits through optimal reservoir release policies. The present study focuses on optimum releases from the reservoir considering heterogeneity of the command area and responses of the command area to the releases instead of minimizing only the reservoir storage volumes. An optimization model has been developed for the reservoir releases based on elitist GA approach considering the heterogeneity of the command area. The developed model was applied to Waghad irrigation project in upper Godavari basin of Maharashtra, India. The results showed that 19% increase in the total net benefits could be possible by adopting the proposed water release policy over the present practice keeping same distribution of area under different crops. The model presented in this study can also optimize the crop area under irrigation. It is found that irrigated area can be increased to 50% of ICA (Irrigable Command Area) from the existing 23% with resulting addition to total net benefits by 31%. The effect of adopting the proposed irrigation schedule and increased irrigation areas would be to increase the net benefits to existing farmers.     

基于最大熵投影寻踪模型的云南省近10 a水资源承载力评价

为对云南省2006—2015年水资源承载力进行评价,从水资源、经济社会和生态系统3个方面提出水资源承载力评价指标体系和分级标准,并基于最大熵投影寻踪(MEPP)技术进行区域水资源承载力评价。采用在指标分级标准阈值间随机生成样本的方法构造MEPP目标函数,利用混合蛙跳算法(SFLA)优化MEPP最佳投影方向,提出SFLA-MEPP水资源承载力评价模型,并构建生物地理优化(BBO)算法-MEPP、和声搜索(HS)算法-MEPP和粒子群优化(PSO)算法-MEPP水资源承载力评价模型作对比模型。结果表明SFLA寻优MEPP目标函数获得的最优值、最劣值、平均值和标准差均优于BBO、HS和PSO算法,具有较好的全局极值寻优能力;SFLA-MEPP模型对云南省2006—2007年、2011—2012年水资源承载力评价为“基本可承载”,其他年份评价为“可承载”;2006—2015年间云南省水资源承载力随时间呈提升趋势,但提升趋势不显著;SFLA-MEPP模型对云南省水资源承载力评价结果与BBO-MEPP模型相同,但在排序上存在差异;与HS-MEPP,PSO-MEPP模型在评价结果及排序上均存在差异。     

Conjunctive Use of Surface Water and Groundwater: Application of Support Vector Machines (SVMs) and Genetic Algorithms

Combined simulation-optimization models have been widely used to address the management of water resources issues. This paper presents a simulation-optimization model for conjunctive use of surface water and groundwater at a basin-wide scale, the Zayandehrood river basin in west central Iran. In the Zayandehrood basin, in the past 10 years, a historical low rainfall in the head of the basin, combined with growing demand for water, has triggered great changes in water management at basin and irrigation system level. The conjunctive use model that coupled numerical simulation with nonlinear optimization is used to minimize shortages of water in meeting irrigation demands for four irrigation systems. Constraints guarantee the maximum/minimum cumulative groundwater drawdown and maximum capacity of irrigation systems. A support vector machines (SVMs) model is developed as a simulator of surface water and groundwater interaction model while a genetic algorithm (GA) is used as the optimization model. Conjunctive use model runs for three scenarios. Results show that the accuracy of SVMs as a simulator for surface water and groundwater interaction model is good and that it is possible to decrease the water shortage for irrigation systems with application of proposed SVMs-GA model.     

Optimization of Applied Water Depth Under Water Limiting Conditions

Water productivity is a major challenge in all agricultural regions and despite water shortages, farmers do not welcome water management strategies due to a lack of knowledge of possible economic consequences. This study aimed to introduce the optimum applied water depths under limiting conditions through mathematical optimization. The effects of optimization were coupled with irrigation scheduling to assess yield, water productivity, and net income. Production and cost functions were created based on two scenarios for the applied water. According to the results, the best applied water depth under water limiting conditions depends on land limiting conditions and how severe water limitation is. In mild to moderate water limiting conditions, by using the optimum applied water depth (W_w), water can be saved by 26% causing a 3 to 4% decrease in the net income per unit of land and a 16% increase in the net income per unit of water. As water supply is severely limited, using the equivalent optimum applied water depth (W_ew) results in the highest productivity. Although using W_ew causes a 14 to 17% decrease in the net income per unit of land, it saves water by 46%. In water limiting conditions, if the land is not limited, using W_ew causes the maximum net income per unit of land. A sensitivity analysis was conducted for the net income and the optimum applied water depths, as well. Furthermore, irrigation scheduling caused a 27% increase in the net income per unit of land. Overall, using optimum applied water depths and irrigation scheduling are highly recommended for addressing water scarcity.

     

Fuzzy Stochastic Genetic Algorithm for Obtaining Optimum Crops Pattern and Water Balance in a Farm

This paper is concerned with multi-objective fuzzy stochastic model for determination of optimum cropping patterns with water balance for the next crop season. The objective functions of the model is to study the effect of various cropping patterns on crop production subject to total water supply in a small farm. The decision variables are the cultivated area of different crops at the farm. The water requirement of the crops follows fuzzy uniform distribution and yields in the objective functions are taken as a fuzzy numbers. The model is solved by using fuzzy stochastic simulation based genetic algorithm without deriving the deterministic equivalents.