An Integrated Approach of System Dynamics,Orthogonal Experimental Design and Inexact Optimization for Supporting Water Resources Management under Uncertainty

An integrated approach of system dynamics (SD), orthogonal experimental design (OED) and inexact optimization modeling was proposed for water resources management under uncertainty. The developed method adopted a combination of SD and OED to identify key scenarios within multiple factors, through which interval solutions for water demands could be obtained as input data for consequential optimization modeling. Also, optimal schemes could be obtained in the combination of inexact two-stage stochastic programming and credibility constrained programming. The developed method was applied to a real-world case study for supporting allocation of multiple-source water resources to multiple users in Dalian city within a multi-year context. The results indicated that a lower credibility-satisfaction level would generate higher allocation efficiency, a higher system benefit and a lower system violation risk. The developed model could successfully reflect and address the variety of uncertainties through provision of credibility levels, which corresponds to the decision makers’ preference regarding the tradeoffs between system benefits and violation risks.     

An Inexact Chance-constrained Quadratic Programming Model for Stream Water Quality Management

Water quality management is complicated with a variety of uncertainties and nonlinearities. This leads to difficulties in formulating and solving the resulting inexact nonlinear optimization problems. In this study, an inexact chance-constrained quadratic programming (ICCQP) model was developed for stream water quality management. A multi-segment stream water quality (MSWQ) simulation model was provided for establishing the relationship between environmental responses and pollution-control actions. The relationship was described by transformation matrices and vectors that could be used directly in a multi-point-source waste reduction (MWR) optimization model as water-quality constraints. The interval quadratic polynomials were employed to reflect the nonlinearities and uncertainties associated with wastewater treatment costs. Uncertainties associated with the water-quality parameters were projected into the transformation matrices and vectors through Monte Carlo simulation. Uncertainties derived from water quality standards were characterized as random variables with normal probability distributions. The proposed ICCQP model was applied to a water quality management problem in the Changsha section of the Xiangjiang River in China. The results demonstrated that the proposed optimization model could effectively communicate uncertainties into the optimization process, and generate inexact solutions containing a spectrum of wastewater treatment options. Decision alternatives could then be obtained by adjusting different combinations of the decision variables within their solution intervals. Solutions from the ICCQP model could be used to analyze tradeoffs between the wastewater treatment cost and system-failure risk due to inherent uncertainties. The results are valuable for supporting decision makers in seeking cost-effective water management strategies.     

漳卫南灌区农业水资源优化配置研究

考虑到不确定条件下漳卫南灌区农业水资源管理的复杂性,为了解决当灌区水资源用户供水目标不能满足需求时的水资源优化配置问题,结合LFP模型与TSP模型的优点,开发了一种分式两阶段随机规划模型(FTSP)。选择漳卫南灌区最大控制性工程岳城水库的两个大型供水灌区作为验证实例,模型应用结果表明,不同决策情景所对应的经济效益和缺水风险不同,最优决策实现了经济效益和缺水风险之间的平衡;不同径流水平下,各用户的正常灌溉面积会发生相应变化,高径流水平时所有用户均能得到正常灌溉。     

An Inexact Two-stage Fuzzy-stochastic Programming Model for Water Resources Management

An inexact two-stage fuzzy-stochastic programming (ITFSP) method is developed for water resources management under uncertainty. Fuzzy sets theory is introduced to represent various punishment policies under different water availability conditions. As an extension of conventional two-stage stochastic programming (TSP) method, two special characteristics of the proposed approach make it unique compared with existing approaches. One is it could handle flexible penalty rates, which are much reasonable for both of the authorities and users, and have seldom been considered in the TSP framework. The other is uncertain information expressed as discrete intervals and probability distribution functions can be effectively reflected in the optimization processes and solutions. After formulating the model, a hypothetical case is employed for demonstrating its applicability under two scenarios, where the inflow is divided into four and eight intervals, respectively. The results indicate that reasonable solutions have been obtained. They provide desired allocation patterns with maximized system benefit under two feasibility levels. The solutions present as stable intervals with different risk levels in violating the water demands, and can be used for generating decision alternatives. Comparisons of the solution from the ITFSP with that from the ITSP (inexact two-stage stochastic programming) and TSP approach are also undertaken. It shows that the ITFSP could produce more system benefit than existing methods and deal with flexible penalty policies for better water management and utilization.     

Identifying Optimal Water Resources Allocation Strategies through an Interactive Multi-Stage Stochastic Fuzzy Programming Approach

In this study, an interactive multi-stage stochastic fuzzy programming (IMSFP) approach has been developed through incorporating an interactive fuzzy resolution (IFR) method within an inexact multi-stage stochastic programming framework. IMSFP can deal with dual uncertainties expressed as fuzzy boundary intervals that exist in the objective function and the left- and right-hand sides of constraints. Moreover, IMSFP is capable of reflecting dynamics of uncertainties and the related decision processes through constructing a set of representative scenarios within a multi-stage context. A management problem in terms of water resources allocation has been studied to illustrate applicability of the proposed approach. The results indicate that a set of solutions under different feasibility degrees (i.e., risk of constraint violation) has been generated for planning the water resources allocation. They can not only help quantify the relationship between the objective-function value and the risk of violating the constraints, but also enable decision makers (DMs) to identify, in an interactive way, a desired compromise between two factors in conflict: satisfaction degree of the goal and feasibility degree of constraints. Besides, a number of decision alternatives have been generated under different policies for water resources management, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic penalties when the promised water-allocation targets are violated, and thus help DMs to identify desired water-allocation schemes under uncertainty.     

An Inexact Two-Stage Water Resources Allocation Model for Sustainable Development and Management Under Uncertainty

In this study, an inexact two-stage water resources allocation (ITWR) model is put forward for supporting sustainable development and management of water resources in Sanjiang Plain, China, which is in such a situation, with multi-water source, multi-water supply subarea, multi-water user and multi-planning goal. The costs of net system, water supply and recourse are analyzed. The developed ITWR model, which shows a strong ability in tacking with various uncertain factors in probability distributions and discrete interval numbers, mixes the techniques of interval-parameter programming (IPP) and two-stage stochastic programming (TSP) within a general optimization framework. And it also has formed an effective link in such a conflict between the policy scenarios and the associated various levels of economic penalties, when the pre-allocation targets of water resources are violated. Based on this model, a series of scenarios under different levels of pre-allocation water is done and different degrees of water surplus and shortage are obtained correspondingly. The results indicate that the reasonable distribution plans with maximum system benefit and minimum system-failure risk have been generated. And these results are valuable for saving water resources to realize its sustainable development and mitigating the penalty to gain economic benefits maximum, and thus some desired results are provided for decision makers in tackling with a complex and uncertain water-resource system.     

A Conditional Value-at-Risk Based Inexact Water Allocation Model

A conditional value-at-risk (CVaR) based inexact two-stage stochastic programming (CITSP) model was developed in this study for supporting water resources allocation problems under uncertainty. A CITSP model was formulated through incorporating a CVaR constraint into an inexact two-stage stochastic programming (ITSP) framework, and could be used to deal with uncertainties expressed as not only probability distributions but also discrete intervals. The measure of risks about the second-stage penalty cost was incorporated into the model, such that the trade-off between system economy and extreme expected loss could be analyzed. The developed model was applied to a water resources allocation problem involving a reservoir and three competing water users. The results indicated that the CITSP model performed better than the ITSP model in its capability of reflecting the economic loss from extreme events. Also, it could generate interval solutions within which the decision alternatives could be selected from a flexible decision space. Overall, the CITSP model was useful for reflecting the decision maker’s attitude toward risk aversion and could help seek cost-effective water resources management strategies under complex uncertainties.     

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.     

Multi-objective stochastic groundwater management of nonuniform,homogeneous aquifers

Like any other resources planning and management, groundwater management is performed in a stochastic environment in which the system itself involves a number of random elements. Consequences as a result of decisions made based on analyses are not certain. This paper presents a management model using the chance-constrained framework which explicitly considers the random nature of aquifer properties. The model enables the derivation of an optimal groundwater management policy that would satisfy required operation performance reliability. Furthermore, the chance-constrained model is extended to the multi-objective optimization framework in which a tradeoff between total water supply pumpage and system performance reliability is explicitly considered. The models are applied to a hypothetical example of a steady, nonuniform, homogeneous confined aquifer.     

水量监控调度信息化系统设计

针对甘肃省张掖市某灌区早期水量监测信息系统的缺陷和不完善性,设计了一套水量监控调度信息化系统。以先进的电子信息技术、网络技术、图像处理等技术为依托,对灌区的灌溉工程设施进行远程控制操作,实时监测各相关工程设施的运行状况,保证了整个系统的实时性和可靠性,达到了节水灌溉的目的。     

Agricultural Multi-Water Source Allocation Model Based on Interval Two-Stage Stochastic Robust Programming under Uncertainty

Numerous uncertainties and complexities exist in the agricultural irrigation water allocation system, that must be considered in the optimization of water resources allocation. In this paper, an agricultural multi-water source allocation model, consisting of stochastic robust programming and two-stage random programming and introducing interval numbers and random variables to represent the uncertainties, was proposed for the optimization of irrigation water allocation in Jiamusi City of Heilongjiang Province, China. The model could optimize the water allocaton to different crops of groundwater and surface water. Then, the optimal target value and the optimal water allocation of different water sources distributed to different crops could be obtained. The model optimized the economic benefits and stability of the agricultural irrigation water allocation system via the introduction of a the penalty cost variable measurement to the objective function. The results revealed that the total water shortage changed from [18.6, 32.3]?×?10⁸ m³ to [15.7, 26.2]?×?10⁸ m³ at a risk level ω from zero to five, indicating that the water shortage decreased and the reliability improved in the agricultural irrigation water allocation system. Additionally, the net economic benefits of irrigation changed from [287.21, 357.86]?×?10⁸ yuan to [253.23, 301.32]?×?10⁸ yuan, indicating that the economic benefit difference was reduced. Therefore, the model can be used by decision makers to develop appropriate water distribution schemes based on the rational consideration of the economic benefit, stability and risk of the agricultural irrigation water allocation system.     

Regional Water Use Structure Optimization Under Multiple Uncertainties Based on Water Resources Vulnerability Analysis

In this paper, a modeling framework by combining system dynamic (SD) model and optimal allocation model was developed to study water resources vulnerability and optimal water use structure, and the framework was applied in the middle reaches of Heihe River basin, northwest of China. The SD model could describe the dynamical change of water resources vulnerability by integrating water resources with socio-economic effect. The sensitivity analysis of SD model was then conducted to design appropriate scenarios for finding out the optimal development pattern, and based on which, an integrated water-saving scenario with lower water resources vulnerability was identified for optimization modeling. Then, an inexact fuzzy-parameter two-stage programming (IFTSP) model was developed and applied to optimize water use structure among industries under uncertainties. This study addresses the water resources vulnerability analysis in considering both water resources system and socio-economic system. Water resources vulnerability analysis was combined with optimization model to make adaptive water resources management plans. And the optimal allocation schemes under lower water resources vulnerability are more advantageous for regional sustainable development.     

Multi-Source Multi-Sector Sustainable Water Supply Under Multiple Uncertainties: An Inexact Fuzzy-Stochastic Quadratic Programming Approach

This paper presents the development and the first application of a superiority–inferiority-based inexact fuzzy-stochastic quadratic programming (SI-IFSQP) approach for sustainable water supply under multiple uncertainties. SI-IFSQP improves conventional nonlinear programming by tackling multiple uncertainties within an individual parameter; SI-IFSQP is also superior to existing inexact methods due to its reflection of economies of scale and reduction of computational requirements. An interactive solution algorithm with high computational efficiency was also proposed. The application of SI-IFSQP to long-term planning of a multi-source multi-sector water supply system demonstrated its applicability. The close reflection of system complexities, such as multiple uncertainties, scale economies and dynamic parameters, could enhance the robustness of the optimization process as well as the acceptability of obtained results. Corresponding to varied system conditions and decision priorities, the interval solutions from SI-IFSQP could help generate a series of long-term water supply strategies under a number of economic, environmental, ecological, and water-security targets.     

Planning for Regional Water System Sustainability Through Water Resources Security Assessment Under Uncertainties

A leader-follower relationship in multiple layers of decision makers under uncertainties is a critical challenge associated with water resources security (WRS). To address this problem, a credibility-based chance-constrained hierarchical programming model with WRS assessment is developed for regional water system sustainability planning. This model can deal with the sequential decision-making problem with different goals and preferences, and reflect uncertainties presented as fuzzy sets. The effectiveness of the developed model is demonstrated through a real-world water resources management system in Beijing, China. A leader-follower interactive solution algorithm based on satisfactory degree is utilized to improve computational efficiency. Results show the that: (a) surface water, groundwater, recycled water, and off water would account for 27.01, 27.44, 23.11, and 22.44% of the total water supplies, respectively; (b) the entire pollutant emissions and economic benefits would consequently decrease by 31.53 and 22.88% when the statue changes from quite safe to extremely far from safe; and (c) a high credibility level would correspond to low risks of insufficient water supply and overloaded pollutant emissions, which lowers economic benefits and pollutant emissions. By contrast, a low credibility level would decrease the limitations of constraints, which leads to high economic benefits and pollutant emissions, but system risk would be increased. These findings can aid different decision makers in identifying the desired strategies for regional water resources management under multiple uncertainties, and support the in-depth analysis of the interrelationships among water security, system efficiency, and credibility level.     

Interval-parameter Two-stage Stochastic Semi-infinite Programming: Application to Water Resources Management under Uncertainty

In this study, an interval-parameter two-stage stochastic semi-infinite programming (ITSSP) method was developed for water resources management under uncertainty. As a new extension of mathematical programming methods, the developed ITSSP approach has advantages in uncertainty reflection and policy analysis. In order to better account for uncertainties, the ITSSP approach is expressed with discrete intervals, functional intervals and probability density functions. The ITSSP method integrates the two-stage stochastic programming (TSP), interval programming (IP) and semi-infinite programming (SIP) within a general optimization framework. The ITSSP has an infinite number of constraints because it uses functional intervals with time (t) being an independent variable. The different t values within the range [0, 90] lead to different constraints. At same time, ITSSP also includes probability distribution information. The ITSSP method can incorporate pre-defined water resource management policies directly into its optimization process to analyze various policy scenarios having different economic penalties when the promised amounts are not delivered. The model is applied to a water resource management system with three users and four periods (corresponding to winter, spring, summer and fall, respectively). Solutions of the ITSSP model provide desired water allocation patterns, which maximize both the system’s benefits and feasibility. The results indicate that reasonable interval solutions were generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of stream flow. The obtained solutions are useful for decision makers to obtain insight regarding the tradeoffs between environmental, economic and system reliability criteria.     

非充分灌溉预报土壤水分下限值的分析研究

根据山西临汾灌溉试验站2009年与2010年冬小麦试验资料,采用田间试验与理论分析相结合的研究方法,利用PS123作物生长模型,计算得出5种水文年型冬小麦潜在蒸发蒸腾量。以产量最大为目标,建立灌溉制度优化模型,确定5种水文年型优化灌溉制度。通过实际应用进行了检验,得出：冬小麦非充分供水条件下灌溉预报土壤水分下限值的理论计算方法,进一步完善了非充分灌溉预报理论,利于灌区动态计划用水与水资源优化配置,对于加强灌区用水管理、提高灌区用水效率具有重大的推动作用。     

Optimization of Industrial Structure Considering the Uncertainty of Water Resources

This paper developed a stochastic linear fractional programming model for industry optimization allocation base on the uncertainty of water resources incorporating chance constrained programming and fractional programming. In this paper, the stochastic linear fractional programming is used in the real word. The development SLFP has the following advantages: (1) The model can compare the two aspects of the targets; (2) The model can reflect the system efficiency intuitively; (3) The model can deal with uncertain issues with probability distribution; (4) The model can give different optimal plans under different risk conditions. The model has a significant value for the industry optimization allocation under uncertainty in local and areas to achieve the maximum economic benefits and the full use of the water resources.     

Water Resources Management and Planning under Uncertainty: an Inexact Multistage Joint-Probabilistic Programming Method

In this study, an inexact multistage joint-probabilistic programming (IMJP) method is developed for tackling uncertainties presented as interval values and joint probabilities. IMJP improves upon the existing multistage programming and inexact optimization approaches, which can help examine the risk of violating joint-probabilistic constraints. Moreover, it can facilitate analyses of policy scenarios that are associated with economic penalties when the promised targets are violated within a multistage context. The developed method is applied to a case study of water-resources management within a multi-stream, multi-reservoir and multi-period context, where mixed integer linear programming (MILP) technique is introduced into the IMJP framework to facilitate dynamic analysis for decisions of surplus-flow diversion. The results indicate that reasonable solutions for continuous and binary variables have been generated. They can be used to help water resources managers to identify desired system designs against water shortage and for flood control, and to determine which of these designs can most efficiently accomplish optimizing the system objective under uncertainty.     

基于作物水分生产函数下的限额灌溉制度优化研究

通过对小麦、玉米、棉花等主要农作物的分阶段受旱试验,获得了三年的限额灌溉试验观测数据;采用非充分灌溉条件下的土壤水分运动理论分析试验数据,建立了限额灌溉条件下的作物蒸发蒸腾模型。结合试验数据分析水分亏缺对作物产量的影响,采用多元回归分析法求解水分生产函数模型参数。采用动态规划法研究了水资源不足条件下的限额灌溉制度的多阶段优化法。研究成果表明,在产量能达到充分灌溉条件下产量的90％的情况下,可节约灌溉用水40％,能为水资源极其短缺地区的农业高效用水提供有力的技术支撑。     

不确定条件下的多水源联合供水调度模型

本文针对城市供水调度系统中存在的不确定性与复杂性,运用区间两阶段随机规划的方法,建立了多水源联合供水调度的优化模型。该模型以供水调度系统成本最小为目标函数,引入概率分布及区间数表示不确定性,模拟了地表水源、地下水源、外来水源等多种水源联合供水过程,并对多种水源的调水目标进行优化。以区间形式给出优化结果,为决策者提供宽裕的决策空间。利用该方法,可充分考虑系统中不确定因素对系统成本的影响,更真实的反映多水源联合供水系统的实际情况。