A Modified Constrained State Formulation of Stochastic Soil Moisture for Crop Water Allocation

In response to uncertainty in crop water allocation, several methodologies have been proposed in the literature, most of them considering rainfall as a stochastic variable affecting soil moisture. A methodology considering uncertainties both in irrigation depth and soil moisture is more realistic for irrigated crops as developed here using an explicit stochastic optimization model. This new work is based on an earlier constrained state formulation which did not consider the irrigation depth as stochastic. In constrained state formulation methods, the first and second moments of state variables are developed considering the uncertainties which are then used as constraints in an optimization model. In contrast to alternative methods that are dynamic programming-based, the proposed optimization method can be solved using standard nonlinear optimization tools. Performance of the proposed model is evaluated for the case of two different crops, winter wheat and barley. Model verification is performed by comparing the results with simulation results. The model is quite acceptable and shows considerable improvement over analogous models.     

Methods to estimate irrigated reference crop evapotranspiration - a review

Efficient water management of crops requires accurate irrigation scheduling which, in turn, requires the accurate measurement of crop water requirement. Irrigation is applied to replenish depleted moisture for optimum plant growth. Reference evapotranspiration plays an important role for the determination of water requirements for crops and irrigation scheduling. Various models/approaches varying from empirical to physically base distributed are available for the estimation of reference evapotranspiration. Mathematical models are useful tools to estimate the evapotranspiration and water requirement of crops, which is essential information required to design or choose best water management practices. In this paper the most commonly used models/approaches, which are suitable for the estimation of daily water requirement for agricultural crops grown in different agro-climatic regions, are reviewed. Further, an effort has been made to compare the accuracy of various widely used methods under different climatic conditions.     

Informational Entropy: a Failure Tolerance and Reliability Surrogate for Water Distribution Networks

Evolutionary algorithms are used widely in optimization studies on water distribution networks. The optimization algorithms use simulation models that analyse the networks under various operating conditions. The solution process typically involves cost minimization along with reliability constraints that ensure reasonably satisfactory performance under abnormal operating conditions also. Flow entropy has been employed previously as a surrogate reliability measure. While a body of work exists for a single operating condition under steady state conditions, the effectiveness of flow entropy for systems with multiple operating conditions has received very little attention. This paper describes a multi-objective genetic algorithm that maximizes the flow entropy under multiple operating conditions for any given network. The new methodology proposed is consistent with the maximum entropy formalism that requires active consideration of all the relevant information. Furthermore, an alternative but equivalent flow entropy model that emphasizes the relative uniformity of the nodal demands is described. The flow entropy of water distribution networks under multiple operating conditions is discussed with reference to the joint entropy of multiple probability spaces, which provides the theoretical foundation for the optimization methodology proposed. Besides the rationale, results are included that show that the most robust or failure-tolerant solutions are achieved by maximizing the sum of the entropies.     

Application of Neural Networks and Optimization Model in Conjunctive Use of Surface Water and Groundwater

This study develops an optimization model for the large-scale conjunctive use of surface water and groundwater resources. The aim is to maximize public and irrigation water supplies subject to groundwater-level drawdown constraints. Linear programming is used to create the optimization model, which is formulated as a linear constrained objective function. An artificial neural network is trained by a flow modeling program at specific observation wells, and the network is then incorporated into the optimization model. The proposed methodology is applied to the Chou-Shui alluvial fan system, located in central Taiwan. People living in this region rely on large quantities of pumped water for their public and irrigation demands. This considerable dependency on groundwater has resulted in severe land subsidence in many coastal regions of the alluvial fan. Consequently, an efficient means of implementing large-scale conjunctive use of surface water and groundwater is needed to prevent further overuse of groundwater. Two different optimization scenarios are considered. The results given by the proposed model show that water-usage can be balanced with a stable groundwater level. Our findings may assist officials and researchers in establishing plans to alleviate land subsidence problems.     

Simulation supported scenario analysis for water resources planning: a case study in northern Italy.

The work presents the results of a comprehensive modelling study of surface and groundwater resources in the Muzza-Bassa Lodigiana irrigation district, in Northern Italy. It assesses the impact of changes in land use and irrigation water availability on the distribution of crop water consumption in space and time, as well as on the groundwater resources. A distributed, integrated surface water-groundwater simulation system was implemented and applied to the study area. The system is based on the coupling of a conceptual vadose zone model with the groundwater model MODFLOW. To assess the impact of land use and irrigation water availability on water deficit for crops as well as on groundwater system in the area, a number of management scenarios were identified and compared with a base scenario, reflecting the present conditions. Changes in land use may alter significantly both total crop water requirement and aquifer recharge. Water supply is sufficient to meet demand under present conditions and, from the crop water use viewpoint, a reduction of water availability has a positive effect on the overall irrigation system efficiency; however, evapotranspiration deficit increases, concentrated in July and August, when it may be critical for maize crops.     

Pressure Management Through Optimal Location and Setting of Valves in Water Distribution Networks Using a Music-Inspired Approach

Pressure management through Pressure Reducing Valves (PRVs) is probably the most used approach related to the leakage management in Water Distribution Networks (WDNs). Its effectiveness in reducing the amount of water losses in existing networks has been highlighted in many papers. In this study, the topic is addressed with particular reference to meta-heuristic optimization techniques, that have proved to be very effective in producing good results with reduced use of computational resources. In particular, the application of the Harmony-Search (HS) method to the location and setting of a pre-fixed number of PRVs is proposed and discussed. A single objective optimization problem is defined which aims at the leakage reduction through the minimization of the water pressures. A double harmonic component is adopted for taking into account both the location and the setting of each PRV. The hydraulic constraints handled by a simulation software are considered as well. The approach is applied to a couple of WDNs: one is the Jowitt and Xu well-known literature test case and the other is a real WDN in Naples, called Napoli Est. The methodology has showed very good results compared to those obtained by using classical Genetic Algorithm techniques both in terms of leakage reduction and computation time.     

Modelling an overdeveloped irrigation system in south India

An optimization model for irrigation planning is developed based on the experience gained from an overdeveloped irrigation system in South India. This model helps the decision maker in choosing the appropriate policy decisions under conditions of shortage of the available water potential to meet the demand of already overgrown irrigation systems. The objective of the model is to maximize the net benefits from crops in the commands of the irrigation projects considered. The constraints of the model include total land limitations of each project, subregional land limitations; reservoir balance, storage capacity, beginning‐year storage constraints for each reservoir; range of possible downstream riparian release policies; sociological constraints regarding essential food crop policy and commercial crop limitations.     

SCE-UA算法在水环境系统优化问题中的应用

把SCE-UA算法应用到水环境系统优化问题的求解中,通过对问题的预处理,求解3个典型的优化问题：①二维稳态水质模型横向扩散系数及流速推求;②排放口最优化处理;③水工建筑物优化调度。结果表明：SCE-UA算法不但能求解一元函数约束的优化问题,通过构造罚函数,还能够成功求解有多元函数约束的优化问题。认为该算法不依赖导数和优化问题的具体形式,所需参数极少,求解精度高,通用性较强,值得在环境系统优化中推广。     

A New Groundwater Management Model by Coupling Analytic Element Method and Reverse Particle Tracking with Cat Swarm Optimization

The performance of groundwater management models mostly depends upon the methodology employed to simulate flow and transport processes and the efficiency of optimization algorithms. The present study examines the effectiveness of cat swarm optimization (CSO) for groundwater management problems, by coupling it with the analytic element method (AEM) and reverse particle tracking (RPT). In this study, we propose two coupled simulation-optimization models, viz. AEM-CSO and AEM-RPT-CSO by combining AEM with RPT and CSO. Both the models utilize the added advantages of AEM, such as precise estimation of hydraulic head at pumping location and generation of continuous velocity throughout the domain. The AEM-CSO model is applied to a hypothetical unconfined aquifer considering two different objectives, i.e., maximization of the total pumping of groundwater from the aquifer and minimization of the total pumping costs. The model performance reflects the superiority of CSO in comparison with other optimization algorithms. Further, the AEM-RPT-CSO model is successfully applied to a hypothetical confined aquifer to minimize the total number of contaminant sources, within the time related capture zone of the wells, while maintaining the required water demand. In this model, RPT gets continuous velocity information directly from the AEM model. The performance evaluation of the proposed methodology, illustrates its ability to solve groundwater management problems.     

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.     

关于加强南水北调中线水源地保护和管理的思考

丹江口水库是南水北调中线工程水源地。通水以来,工程持续安全平稳运行。然而,由于丹江口水库面积大,范围广,涉及陕西、河南和湖北省3省,在水源地保护和管理方面仍存在亟需解决的问题,包括:①水库管理薄弱,如水库管理设施缺乏、水库保护和管理经费尚无渠道落实、消落区管理难度大等;②部分入库支流水质难以达标及入库支流总氮负荷高,如2017—2018年神定河和泗河入库断面水质仍为Ⅴ类甚至劣Ⅴ类,入库支流总氮浓度在2~10 mg/L之间;③中线水源地保护和管理的体制和机制仍未明确,相关法律法规尚不健全。针对水源地保护和管理中存在的突出问题,提出建设必要的水库管理设施和核增水库保护和管理经费,规范消落区的保护和利用模式,明确入库支流水质改善和提升监督管理办法及总氮负荷削减措施,以及尽快制定并颁布《南水北调中线水源地保护与管理条例》等相关法规。     

Simulation and Multi-Objective Management of Coastal Aquifers in Semi-Arid Regions

Groundwater is the main water resource in many semi-arid coastal regions and water demand, especially in summer months, can be very high. Groundwater withdrawal for meeting this demand often causes seawater intrusion and degradation of water quality of coastal aquifers. In order to satisfy demand, a combined management plan is proposed and is under consideration for the island of Santorini. The plan involves: (1) desalinization (if needed) of pumped water to a potable level using reverse osmosis and (2) injection into the aquifer of biologically-treated waste water. The management plan is formulated in a multi-objective, optimization framework, where simultaneous minimization of economic and environmental costs is desired, subject to a constraint so that cleaned water satisfies demand. The decision variables concern the well locations and the corresponding pumping and recharging rates. The problem is solved using a computationally efficient, multi-objective, genetic algorithm (NSGAII). The constrained multi-objective, optimization problem is transformed to an unconstrained one using a penalty function proportional to constraint violation. This extends the definition of the objective function outside the domain of feasibility. The impact of prolonged droughts on coastal aquifers is investigated by assuming various scenarios of reduced groundwater recharge. Water flow and quality in the coastal aquifer is simulated using a three-dimensional, variable density, finite difference model (SEAWAT). The method is initially applied to a test aquifer and the trade-off curves (Pareto fronts) are determinedl for each drought scenario. The trade-off curves indicate an increase on the economic and environmental cost as groundwater recharge reduces due to climate change.     

Optimal Short-term Reservoir Operation with Integrated Long-term Goals

A methodology to incorporate long-term goals within the short-term reservoir operation optimization model is proposed. Two conflicting objectives for the management of hydropower generation in two different power plants are incorporated. A chance-constrained optimization model is used to derive long-term (annual) operation strategies. With the time horizon of operation for the short-term optimization model kept equal to a single time-step of the long-term optimization model, the optimum end storages derived from the long-term model are incorporated as constraints (storage lower bounds) within the short-term model. The long-term benefits accrued from such an operation model are illustrated for a small reservoir, in South India. The solutions are compared with the historic operation. These are also compared with the solutions of a short-term optimal operation model ignoring long-term goals. The optimization model is solved using a multi-objective genetic algorithm.     

Socially-Optimal and Nash Pareto-Based Alternatives for Water Allocation under Uncertainty: an Approach and Application

This study has proposed a methodology by enhancing an interactive algorithm to multi-objective optimization problems with interval parameters, in an attempt to reach the tradeoff between quality and reliability of the resultant optimum solutions. The earlier algorithm could turn into a prolonged procedure that deals with several players with different aspirations at different reliability, or risk, levels under non-deterministic conditions. Hence, it is not a pertinent approach to solve problems of water allocation between competing parties. The enhanced methodology aims to alleviate the burdens of the procedure and generate a unique set of solutions (i.e., near-Pareto-optimal alternatives), instead of a myriad of compromise solution sets. We have investigated a real-world hydro-environmental problem, the allocation of water between Dorudzan-Korbal irrigation networks and Bakhtegan Lake in Fars Province, Iran to assess feasibility of this methodology. In order to reach a consensus concerning the stakeholders’ individual preferences, we identified the compromise alternatives from the obtained sets of non-dominated solutions by taking advantage of various social choice rules and the Nash bargaining model. The results demonstrated that the developed methodology could incorporate the risk of system constraints violations (i.e., planning reliability under uncertainty) into the process of approximating the optimal tradeoff set of solutions. It also gave policymakers a chance to acquire perception into the potentially best compromise for land and water allocation schemes regarding the preference profiles of the involved interest groups.     

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.     

Multi-Reservoir System Optimization using Chlorophyll-<Emphasis Type="Italic">a</Emphasis> Trophic Indexes

The problem of managing water scarcity by resorting to complex interconnected multi-source water systems needs to utilize management optimization techniques analyzing aspects of water quantity and quality in a common strategy. In the southern regions of Mediterranean Europe, the greater part of water resources for supply systems are derived from artificial reservoirs and water systems can become quite complex since they interconnect several sources and demand centers, as indeed occurs in the Region of Sardinia (Italy). A simplified approach to the requirement to insert water quality aspects in the mathematical optimization model can be achieved by examining the trophic conditions of reservoirs. Trophic State Indexes (TSI) based on Carlson’s (1977) make it possible to insert quality constraints in the water management optimization model also considering complex multi-reservoir and multi-user systems. Model formalization and implementation by the optimization toolkit WARGI (Water Resource system optimization aided by Graphical Interface) (Sechi and Zuddas 2000; Manca et al. 2004; Salis et al. 2005) is illustrated in this paper. The usefulness of a mixed quantity–quality optimization approach has been confirmed by WARGI application to a real multi-reservoir water resources system in southern Sardinia. The optimization model remains computationally efficient dealing with this complex multi-reservoir system and the obtained results can be seen as a reference target in a subsequent simulation phase.     

基于遗传算法的变密度条件下地下水模拟优化模型

将遗传算法和变密度地下水流及溶质运移模拟程序SEAWAT耦合起来,开发了一个新的用于地下水模拟优化管理的通用程序——SWTGA。以求解变密度条件下地下水优化管理问题,从而为地下水管理决策者提供科学依据和技术支持。设计SWTGA时,建立了适用于变密度条件下地下水优化管理常见问题的目标函数的一般形式,同时设定了常用的约束条件。最后将SWTGA程序应用于一个理想滨海含水层中地下水开采方案的优化设计,寻优之后获得了最佳开采方案,与未优化开采方案的对比显示优化结果合理可行,验证了SWTGA模拟优化程序的有效性和可靠性。     

Better Water and Land Allocation for Long-term Agricultural Sustainability

Water and land resources are limited and dwindling in quantity and quality due to pollution and the effects of climate change. The "world needs to produce over sixty percent more food to feed" its 9.9 billion population in 2050 using these dwindling resources. Increased food production is also necessary to achieve most of the "UN's SDGs such as SDG1 (No Poverty), SDG2 (Zero Hunger), SDG3 (Good Health and Well-Being), and SDG15 (Life on Land)", etc. The aforesaid "goal can be accomplished by optimizing the distribution of available water and land resources, which can be done through an optimization model". In this study, a water balance model was first developed "to assess the long-term groundwater recharge, which will help to understand the dynamics of the system". Then, after analyzing the results, an optimization model was formulated to maximize the net annual farm income in an irrigated region of India. The water balance model showed excellent results as indicated by "high R-squared (0.9728) and model efficiency (0.91)", and low RMSE (0.2516 m) and ME (-0.0526 m) values. The water balance analysis revealed "that the aquifer level has been rising at a steady rate" over the past two decades. The results of the water balance model were used to formulate various constraints of the optimization model. Under the optimal cropping system, the area of paddy decreases against an increase in the area of sorghum, pearl millet, and cotton during the monsoon. Whereas "during the winter, the area of wheat increases", and the area of mustard and barley decreases. Groundwater "abstraction has increased, eventually lowering the aquifer level and thus alleviating salinization and waterlogging problems in the region". Net yearly income in the region enhanced by more than twenty-two percent to ?821.24 million from the present ?671.33 million. The sensitivity analysis revealed that the crops' market price is the most sensitive factor in the optimization model. "It is recommended that government agencies and real-world agricultural farmers practice increased use of groundwater in conjunction with canal water to maximize farm income. The approach used is the first of its kind in the region under study, is easy to apply, and can be replicated in other regions of the world" dealing with comparative issues.

     

Irrigation Planning and Management Through Optimization Modelling

Proper planning and management of irrigation is vital in achieving food security for the burgeoning global population and sustaining livelihoods. Because irrigated agriculture is expected to provide more food, if managed properly. The comprehensive reviews on the use of various programming techniques used for planning and management of irrigation have been provided in this paper. The literature review revealed that the management models used in the past mainly considered the objectives of maximization of net farm income, minimization of waterlogging, and minimization of groundwater depletion. These objectives were achieved by optimizing the allocation of available land and water resources. The past reviews are grouped into four sections based on the programming techniques adopted. The sections include: linear programming, nonlinear programming, dynamic programming, and genetic algorithms. This review provides the basis for the selection of appropriate methodology for the planning and management of irrigation.     

Irrigation water requirement models of some major crops

A case study of the Mahanadi-Kathjori-Devi delta of Orissa, India was undertaken for the formulation of irrigation water requirement models of some major crops. Principal crops such as paddy (both during the Kharif and Rabi Seasons), sugarcane, jute, chilly, mustard, groundnuts, etc., which are mostly grown in the delta, were taken into account for the present study. Effective rainfall and irrigation water requirements are the two basic parameters of the developed models. The models help in predicting irrigation water requirements for different crops once the values of effective rainfalls are ascertained. This irrigation water requirement, estimated by models so developed, includes water requirements for consumptive use only and when any other water used for special requirements such as leaching, seed bed preparation, etc., is added to it, the total irrigation water requirement is determined. The models so developed can be used for the same crops grown in other areas having the same hydrometeorological characteristics.