A Multi-objective Linear Programming Model with Interval Parameters for Water Resources Allocation in Dalian City

Water resources management has been of concern for many researchers since the contradiction between increased water demand and decreased water supply has become obvious. In the real world, water resources systems usually have complexities among social, economic, natural resources and environmental aspects, which leads to multi-objective problems with significant uncertainties in system parameters, objectives, and their interactions. In this paper, a multi-objective linear programming model with interval parameters has been developed wherein an interactive compromising algorithm has been introduced. Through interactive compromising conflicts among multi-objectives, a feasible solution vector can be obtained. The developed model is then applied to allocation of multi-source water resources with different water qualities to multiple users with different water quality requirements for the Dalian city for 2010, 2015 and 2020 planning years. The model pursues the maximum synthesis benefits of economy, society and the environment. The results indicate that the proportion of reused water to the total water amount is gradually increasing, and the proportion of agricultural water consumption to the total water consumption is gradually decreasing. The allocation of multi-source water resources to multiple users is improved due to incorporation of uncertain factors into the model that provide useful decision support to water management authorities.     

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.     

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.     

IFTSQP: An Inexact Optimization Model for Water Resources Management under Uncertainty

Abstract

An interval-fuzzy two-stage quadratic programming (IFTSQP) method is developed for water resources management under uncertainty. The methodincorporates techniques of interval-parameter programming, two-stage stochastic programming, and fuzzy quadratic programming within a general optimization framework to tackle multiple uncertainties presented as intervals, fuzzy sets and probability distributions. In the model formulation, multiple control variables are adopted to handle independent uncertainties in the model's right-hand sides; fuzzy quadratic terms are used in the objective function to minimize the variation in satisfaction degrees among the constraints. Moreover, the method can support the analysis of policy scenarios that are associated with economic penalties when the promised targets are violated. The developed method is then applied to a case study of water resources management planning. The results indicate that reasonable solutions have been obtained. They can help provide bases for identifying desired water-allocation plans with a maximized system benefit and a minimized constraint-violation risk.     

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.     

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.     

A Hybrid Dynamic Dual Interval Programming for Irrigation Water Allocation under Uncertainty

Along with the economic development in Canada, the shortage of irrigation water has become a serious concern (Bouwer 1993; Hennessy 1993). In this study, a model of Dynamic Dual Interval Programming (DDIP) is developed and applied to the irrigation water allocation systems with uncertainty. DDIP method improves the existing dynamics interval programming by explicitly addressing the system uncertainties with a dual interval that had higher system reliability. The solution of DDIP is computationally effective, and its decision variables are incorporated into the solutions for final decision. In order to obtain the optimal allocation schemes in a dynamic process, the developed DDIP was applied to an irrigation water system. The results from this case study revealed that optimal solution can be obtained through the DDIP approach from the agriculture water management activities for feasible decisions. These decisions reflect the high uncertainty of the information in the boundaries of dual intervals. The solution presents a maximum benefit under limited yearly uncertain natural resources. Furthermore, the information obtained though this model may help the authority to make optimal decisions and to reduce the risk for uncertain situations.     

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.     

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.     

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.     

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.     

Estimating water deficit and its uncertainties in water-scarce area using integrated modeling approach

Accurate assessment of water deficit and related uncertainties in water-scarce areas is strategically important in various fields of water resources management. This study developed a hybrid approach integrating conceptual water balance model and econometric regression to estimate water shortage and its related uncertainties in water-scarce areas. This hybrid approach was used to assess the agricultural water deficit of Beijing, an extremely water-scarce area in China. A predictive model of agricultural water demand was developed using the stepwise multiple regression method, and was validated by comparing the predicted values with observed data. Scenario analysis was employed to investigate the uncertainties of agricultural water shortage and agricultural water demand. This modeling approach can assist water administration in creating sustainable water allocation strategies in water-scarce areas.     

The Impact of Uncertainty Factors on Optimal Sizing and Costs of Low-Impact Development: a Case Study from Beijing,China

The facility allocation optimization of Low-impact development (LID) optimization has been used widely to prevent and tackle urban storm water pollution. However, uncertainties existing in nature and human society would influence the size and total cost of LID. To study the influence of the uncertainties on LID optimization allocation, the research develops the model of LID optimization allocation under uncertainty. The principle of the model is establishing primarily the LID optimization model based on certain numbers and identifying the uncertainties. Hence, the model integrates the uncertainty programming, including interval programming, fuzzy programming, stochastic programming, chance constraint programming (CCP) and scenario programming. The model of LID optimization allocation under uncertainty is established with the conditions. The developed uncertainty model tackles multiple types of uncertainties, and the results of the model are in the interval form in multiple scenarios. The model analyses the effects of uncertainties on the size and total cost of LID in this way. The study shows that the uncertainties in rainfall, infiltration rate, release coefficient, funds and unit price all have a significant influence on the size and total cost of LID when these uncertainty factors overlay. A higher violation probability of CCP corresponding to LID sizing results to a wider interval number of the corresponding uncertainty. The developed method of the study is universal, and the method could be extended to other cases of LID optimization allocation to speculate the influence of uncertainties.     

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.     

试论塔里木河流域综合治理中的水权管理

在阐述塔里木河流域水资源概况及近期综合治理目标的基础上,研究了流域水权的科学划分,确定了塔里木河流域水权分配原则,分析了塔里木河流域水权分配方案和“四源一干”天然生态水权分配方案,对塔里木河的水资源定额管理体系的建立,以及水权管理系统和水资源统一管理管理前提下的水权管理进行了阐述。所涉及的水权问题为水资源的使用权。     

Optimal Long-term Operation of Reservoir-river Systems under Hydrologic Uncertainties: Application of Interval Programming

Long-term basin-wide reservoir-river operation optimization problems are usually complex and nonlinear especially when the water quality issues and hydrologic uncertainties are incorporated. It is due to non-convex functions in water quality modeling and a large number of computational iterations required by most of stochastic programming methods. The computational burden of uncertainty modeling can be reduced by a special combination of uncertainty modeling and interval programming, though the problem solution is still a challenge due to model nonlinearity. In this paper, an integrated water quantity-quality model is developed for optimal water allocation at river-basin scale. It considers water supply and quality targets as well as hydrologic, water quality and water demand uncertainties within the nonlinear interval programming (NIP) framework to minimize the slacks in water supply and quality targets during a long-term planning horizon. A fast iterative linear programming (ILP) method is developed to convert the NIP into a linear interval programming (LIP). The ILP resolves two challenges in NIP, first converting the large non-linear programming (NLP) into a linear programming (LP) with minimum approximation and second reducing the iterations needed in interval programming for NLP into just two iterations for the upper and lower limits of decision variables. This modeling approach is applied to the Zayandehrood river basin in Iran that has serious water supply and pollution problems. The results show that in this river basin at dry conditions when available surface water resources are below 85 % of normal hydrologic state and water demands are 115 % of current water demands, the total dissolved solids (TDS) concentration can be reduced by 50 % at the inlet of the Gavkhuni wetland located downstream of the river basin.     

Basin Initial Water Rights Allocation under Multiple Uncertainties: a Trade-off Analysis

Economic losses and inequities caused by uncertainties in the availability of water intensify the competition between water sectors, making the allocation of water rights of vital importance for minimizing water conflicts. In this study, an Interval-parameter Two-stage Stochastic Programming (ITSP) model for water rights allocation is developed that contains an industrial allocation preference coefficient and involves the risk control of Conditional Value-at-Risk theory and Gini coefficient constraints (ITSP-CG). Using China’s Taihu Basin as a case study, it is shown that optimized water rights allocation schemes can reduce the risk of inequitable localized water deficits, a narrower confidence interval results in higher economic loss, and, when the confidence level is fixed, tighter control of water availability results in water efficient sectors having an increasing preference for allocation schemes. It is also shown that Basin Authorities need to trade-off the equitable allocation of water rights and economic returns over a particular planning period.

     

A Decision Support System to Improve Water Resources Management in the Conchos Basin

The Conchos basin is the largest tributary to the lower part of the Rio Grande/Rio Bravo basin. During recent years a severe drought has affected México’s ability to deliver water from the Conchos basin as required by the 1944 Treaty. In addition, it has generated not only economic problems in the USA and México but also political frictions between these two countries. The Mexican Conchos river has historically contributed with the highest amount of water to USA as established on the water treaty. A Decision Support System (DSS) was developed for the Conchos basin in order to gain a better understanding of the water resources management process in the basin, and to identify the alternatives to improve the cited process. The DSS is a semi-distributed model, based on System Dynamics, and developed using Powersim software. The DSS has been used to evaluate 25 long and short tem water resources allocation alternatives for the two main basin’s users: Irrigation Districts and Water Treaty. Some of the most important factors being tested on the 25 water management alternatives include National Commission of Water’s yearly water allocation policy, reservoir operation rules, improvement on water distribution efficiencies, etc. The DSS model shows that the historic water resources allocation implemented by the Federal government produces adequate results as compared with the other tested water management alternatives. However, for short term drought scenarios, it is showed that there could be other management alternatives that could perform better than the current water management allocation. In general, the DSS shows what we already expect of dynamic models of systems to provide that understanding the effects of multiple interacting variables in necessary to develop good natural resource management policies.     

Basin Scale Water Resources Systems Modeling Under Cascading Uncertainties

Global change in climate and consequent large impacts on regional hydrologic systems have, in recent years, motivated significant research efforts in water resources modeling under climate change. In an integrated future hydrologic scenario, it is likely that water availability and demands will change significantly due to modifications in hydro-climatic variables such as rainfall, reservoir inflows, temperature, net radiation, wind speed and humidity. An integrated regional water resources management model should capture the likely impacts of climate change on water demands and water availability along with uncertainties associated with climate change impacts and with management goals and objectives under non-stationary conditions. Uncertainties in an integrated regional water resources management model, accumulating from various stages of decision making include climate model and scenario uncertainty in the hydro-climatic impact assessment, uncertainty due to conflicting interests of the water users and uncertainty due to inherent variability of the reservoir inflows. This paper presents an integrated regional water resources management modeling approach considering uncertainties at various stages of decision making by an integration of a hydro-climatic variable projection model, a water demand quantification model, a water quantity management model and a water quality control model. Modeling tools of canonical correlation analysis, stochastic dynamic programming and fuzzy optimization are used in an integrated framework, in the approach presented here. The proposed modeling approach is demonstrated with the case study of the Bhadra Reservoir system in Karnataka, India.     

Risk Assessment for Ecological Planning of Arid Inland River Basins Under Hydrological and Management Uncertainties

The Shiyanghe river basin, an arid inland basin of northwest China, is taken as an example to analyze the risk for achieving the ecological planning objective in arid inland river basins under uncertainty conditions. Hydrology and management uncertainties that affect the accomplishment of ecological planning objective are analyzed quantitatively with the methods of Bayesian theory based Probabilistic model, scenario analysis and interval analysis. Bayesian probabilistic analysis method was used to analyze the hydrological uncertainties in the form of probability and interval distributions in planning period, while the scenario analysis method and interval method were used to analyze the managing uncertainties in the form of interval numbers. Instead of the ecological risk analysis, which for arid inland river basin, of studying the impact of environmental and human factor on ecological system, water resources and environment, we focused on analysing the possible impact of hydrological and management uncertainty factor on the ecological planning, and forecasting the degree of the completion under the uncertainty. Our study provided the probabilities of achieving ecological planning objective and the possible deviation of different scenarios. The more local water resources and higher level of local water resource utilization and management appeared to lead higher probability to achieve the ecological objective. This study can help environment and water resource managers and planner to formulate a rational planning for arid inland river basins under hydrological and management uncertainty.