The Application of a Water Rights Trading Model Based on two-Stage Interval-Parameter Stochastic Programming

In recent decades, increasing conflicts have occurred between different water users because of issues related to water resource allocation. To better allocate water resources, this study adopts the latest water rights trading model instead of the traditional water resources allocation model and combines it with a two-stage interval-parameter stochastic programming model (TISP). The combined model was applied to the Sanjiang Plain with multi-area, multi-source, and multi-water users, and the optimized promised water availability for every user in the area was obtained. The analysis shows that a higher promised water availability corresponds to a higher benefit but also promotes additional risks because of water shortages, whereas a lower promised water availability corresponds to a lower benefit, although it is more likely to result in water waste at high inflow levels. The combined model formed an effective link between water use benefits and water deficiency punishments that enabled us to obtain an optimized promised water availability that considers benefits and risks simultaneously. The second result is that the water rights trading model can improve the utilization efficiency of water resources, which are typically transferred from inefficient water use areas to efficient water use areas, resulting in a benefit to all areas. Additionally, in the water rights trading model, self-profit motivation at a micro-level can emerge in adaptation at a macro-level, which stabilizes the water resource system and reduces the possibility of system collapse because of extreme water shortages.     

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.     

A Stochastic Non-linear Programming Model for a Multi-period Water Resource Allocation with Multiple Objectives

A rapid increase in demand and severe droughts in recent years has increased the pressure on water supplies throughout most parts of Australia. This has resulted in the need for tools to allocate limited water across users in different regions, and explore scenarios so as to achieve economic, social and environmental benefits. A major challenge in water resource allocation is dealing with the uncertainty in the system, particularly with respect to reservoir inflow. Stochastic non-linear programming is applied to water resource allocation to accommodate this uncertainty across the time periods of the planning horizon. A large range of solutions is produced representing the distributions of uncertainty in reservoir inflow. These solutions are used in a Monte Carlo simulation to estimate the trade-off in amounts of water allocated versus risk of not achieving minimal reservoir levels. The methodology is applied to a case study in South East Queensland in Australia, a region which is currently facing a severe water shortage over the next 3 years. A new water supply initiative that the Queensland State Government is considering to overcome the water crisis is assessed using the methodology.     

Interval-parameter Two-stage Stochastic Nonlinear Programming for Water Resources Management under Uncertainty

Over the past decades, controversial and conflict-laden water allocation issues among competing interests have raised increasing concerns. In this research, an interval-parameter two-stage stochastic nonlinear programming (ITNP) method is developed for supporting decisions of water-resources allocation within a multi-reservoir system. The ITNP can handle uncertainties expressed as both probability distributions and discrete intervals. It can also be used for analyzing various policy scenarios that are associated with different levels of economic consequences when the promised allocation targets are violated. Moreover, it can deal with nonlinearities in the objective function such that the economies-of-scale effects in the stochastic program can be quantified. The proposed method is applied to a case study of water-resources allocation within a multi-user, multi-region and multi-reservoir context for demonstrating its applicability. The results indicate that reasonable solutions have been generated, which present as combined interval and distributional information. They provide desired water allocation plans with a maximized economic benefit and a minimized system-disruption risk. The results also demonstrate that a proper policy for water allocation can help not only mitigate the penalty due to insufficient supply but also reduce the waste of water resources.     

邯郸市水资源管理与开发模式

由于水资源短缺,加强管理对于邯郸市水资源可持续利用具有重要意义。在分析邯郸市水资源现状与主要的水资源管理措施的基础上,研究了邯郸市的水资源管理模式,指出邯郸市水资源模式的主要特点是以流域管理为主,计划用水与水费管理、水权交易相结合。并对邯郸市水资源管理模式进行了分区探讨,初步提出了西部山区蓄引提综合利用、中部平原井渠结合与东部平原地下微咸水利用三种基本模式。指出山区蓄引提综合利用模式的主要特点是以蓄为主、蓄引提相结合、充分利用水资源;井渠结合模式的特点是地表水与地下水联合调度,井渠结合,井灌井排,提高大气降水、地表水、地下水的利用;微咸水利用模式的基本特点是挖掘浅层地下水潜力,咸淡水轮灌或浑灌。从而为邯郸市的水资源高效利用提供了一定的指导建议。     

分布式水文模型在水资源管理中的应用

水资源管理是目前世界水环境的主要问题,是痧资源可持续发展的关键,而水模型则是水资源管理的依据,本根据国外水模型资料,从农业灌溉,水土流失、土地利用等几个方面介绍了目前水平资源管理模型的应用现状及其发展的限制因素,分布式水模型是今后水资源管理模型的发展方向。     

Stochastic Goal Programming Model for Optimal Blending of Desalinated Water with Groundwater

A stochastic goal programming (GP) model is developed in orderto determine the daily production of desalination plants to meet the requirements of water blending stations (WBS) for major cities in the Eastern Province of the Kingdom of SaudiArabia. The WBS is assumed to be a control point in the systemwhere water is blended to satisfy the desired water quality, downstream of the control point. The desalinated water is blended with brackish groundwater extracted from several groundwater wells. The objective of the model is to minimize the goal deviations from the following priority levels: demand for blended water, control of salinity levels, depletion of groundwater and maximize the use of brackish water, demand forbrackish water at WBS, and production of desalinated water. Anessential element of the model is the input data; unfortunately,available data are not accurate due to the inherent uncertaintyassociated with it. This uncertainty will generate uncertainty in the model output, which affects reliability and confidence associated with the decisions. Thus, reliable planning should consider uncertainties associated with model input parameters.The developed stochastic model shows how Goal Programming (GP)modeling can be used to plan the water resources in the EasternProvince of Saudi Arabia, assuming that both supply and demandare uncertain.     

A New Interval Two-stage Stochastic Programming with CVaR for Water Resources Management

Water Resources Management - In area of water resources management, decision-makers usually need to make plans under various uncertainties in order to achieve the maximal total net benefits and...     

Two-Stage Multi-Water Sources Allocation Model in Regional Water Resources Management under Uncertainty

Water Resources Management - Water shortages are common in society, and the effective allocation of limited water resources to each competitive sector has become one of the urgent concerns for...     

Facilitation Model for Supporting Integrative Water Resource Management

In this paper, the Soft Systems Methodology foundations was integrated to a water resources management model to construct a facilitation process to support integrative water resources management. The facilitation model aims to achieve agreements over conflicts, by promoting engagement of actors to construct a common and holistic understanding about the problematic situation through an interactive, participative, clear, and transparent process. The facilitation model is divided into two phases: (i) Preparing Logistics and (ii) Work meeting. The first phase starts when a conflict emerges, then, the facilitator should prepare the logistics necessary to proceed with the examination of the problem, including the selection of volunteers who will compose the work group. The second phase is intended to the application of an interactive and participatory modeling approach organized into five steps: 1 - Expressing the problematic situation; 2 - Constructing a multi-sectoral representation; 3 - Examining the multi-sectoral representation; 4 - Building a conceptual model; and 5 - Exploring actions. To illustrate its application, we applied these steps to structure a real complex problematic situation that exists in an area of the watershed of the Paraiba do Norte River, in Northeastern Brazil. To construct a multi-sectoral representation of the problem, perspectives of different actors who are involved in the problem were considered. We conclude that the model is a powerful tool for the analysis of complex problems, for which there is no consensus regarding alternatives to solve/mitigate it and/or there is a high level of uncertainties involved in the decision-making process.

     

A Compromise Programming Model to Integrated Urban Water Management

Integrated urban water management is an important and critical matter in every city and country. Many objectives and criteria such as satisfaction of the urban water consumers, the national benefits and social hazards must be considered in the integrated urban water management. So the integrated urban water management can be considered as a multi-objective problem. In this paper, a mathematical model which uses the compromise programming model is presented to optimize this multi-objective problem. Three famous objectives involving water distribution cost, leakage water and social satisfaction level are considered. To evaluate the performance and efficiency of the proposed model, Hamedan potable water network is chosen as a case study. Results show that the proposed model is capable to present effective solutions for the considered problem. So the proposed mathematical model can be used as an efficient tool for the integrated urban water management in every urban area.     

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.     

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.     

基于适应性管理的水污染生态补偿体系框架

生态补偿作为水污染控制与管理的手段之一,可以有效减少排污现象,提高水资源的利用效率。基于法律、环境以及经济学角度对水污染生态补偿内涵进行了研究,运用系统工程理论与方法研究水污染生态补偿系统的构成要素与结构,分析该补偿系统的复杂性,并基于应对复杂性和不确定性问题的适应性管理理论,构建了包括前提条件、主体、手段以及载体的水污染生态补偿适应性管理体系框架。通过该适应性管理框架,可以辨识该补偿系统中各参与主体的微观行为机理,调动其积极性,协调相互之间的关系,提高整个系统的学习能力与适应能力,从而合理制定补偿方案,提高水污染治理的效率与效果。     

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.     

基于遗传程序的南水北调中线水面线计算

目前南水北调中线水面线的推算采用传统恒定非均匀流公式,糙率等水力参数需根据实际情况,定期复核并进行修正,缺乏灵活性。结合中线调度实际,建立了基于遗传程序设计的渠道水面线计算模型,利用遗传程序自动拟合渠段上游节制闸闸后水位与下游节制闸闸前水位、输水流量的非线性关系,从而实现在闸前常水位控制模式下不同输水流量的水面线推算。经在南水北调中线干线工程典型渠段的试用表明,建立的基于遗传程序水面线计算模型灵活性强、拟合精度高、操作方便,具有较高的实际应用价值。     

A Fuzzy Robust Nonlinear Programming Model for Stream Water Quality Management

An interval-parameter fuzzy robust nonlinear programming (IFRNP) approach was developed for stream water quality management under uncertainty. The interval and fuzzy robust programming methods were incorporated within a general framework to address uncertainties associated with the nonlinear objective and the left- and right-hand sides of the constraints. A piecewise linearization approach was developed to deal with the nonlinear cost function. IFRNP could explicitly address complexities of various system uncertainties, where parameters were represented as both interval numbers and fuzzy membership functions. Furthermore, the dual uncertain information associated with the lower and upper bounds of each interval parameter could be effectively tackled through the concept of fuzzy boundary interval. The proposed IFRNP method was applied to a case of water quality management in the Guoyang section of the Guo River in Anhui province, China. A number of cost-effective schemes for water quality management were generated, and allowable wastewater discharge amounts were recommended. The results indicated that IFRNP was applicable to water quality management problems, where high nonlinearities and dual uncertainties exist.     

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.     

SWAT模型在海河流域水资源管理中的应用

海河流域水资源过度开发利用,已造成严重的水环境危机,实现流域水资源可持续利用显得尤为重要。针对海河流域,利用SWAT模型构建流域、子流域、县域不同层次的水文模型,根据过去、现状水资源水环境的变化过程,预测未来各种情景下的水资源水环境变化趋势,为流域、区域的水资源管理提供科学依据。     

An Improved Solving Approach for Interval-Parameter Programming and Application to an Optimal Allocation of Irrigation Water Problem

In this study, an improved single-step method (SSM) is developed based on two-step method (TSM) to solve the interval-parameter linear programming (ILP) model of which the right-hand sides are highly uncertain. Two numerical examples are presented to ascertain appropriate value of λ in SSM. The risk preference degree of λ could be 0.8 for maximum objective function type. To demonstrate the applicability of the developed method, an agricultural water management problem has been provided in the case study section. The results show that SSM is more effective than TSM for complete solutions. There is only partial solution obtained from the first submodel of TSM, because the right-hand side of the wheat output constraint is highly uncertain. Finally, local farmers’ net benefit reaches to [8.949, 12.442]?×?10⁸ RMB (the unit of Chinese currency). The priority order of crops that are needed to be irrigated by surface water is maize > wheat > cotton.