Zhangweinan River Basin Research Articles

Conjunctive water management under multiple uncertainties: A centroid-based type-2 fuzzy-probabilistic programming approach

In this study, a centroid-based type-2 fuzzy-probabilistic programming (CT2FP) approach is developed for supporting conjunctive use of surface water and groundwater under multiple uncertainties. CT2FP can not only tackle uncertainties expressed as type-2 fuzzy sets (in both objective function and constraints) but also address complexity with characteristic of randomness and two-layer fuzziness (i.e., type-2 fuzzy random variables). Solution method based on α-plane theory, enhanced Karnik–Mendel algorithm (EKM) and interactive algorithm are proposed to transform type-2 fuzzy-probabilistic constraints into their deterministic equivalents. A case study in Zhangweinan River Basin (China) is used to demonstrate the applicability of the proposed approach. Scenarios associated with different constraint-violation risk levels are examined to generate applicable cropping patterns and water-allocation schemes. The amount of groundwater used for irrigation can be determined (i.e., more than [462.84, 495.78] × 106 m3 in dry season and no more than [470.83, 537.19] × 106 m3 in wet season, respectively) to address the conflict between food security and ecological protection. The relationship among crop area, water allocation, and economic benefit can be reflected to enhance the agricultural sustainable development for the study basin.

A Type-2 Fuzzy Based Interval-Stochastic Risk Management Programming Model for Identifying Sustainable Water Resources Allocation Policies

In this study, a type-2 fuzzy based interval-stochastic risk management programming (TISRM) method is proposed for optimal irrigation water resources allocation associated with multiple uncertainties expressed as interval numbers, probability distributions, and type-2 fuzzy sets. TISRM can also reflect authorities’ attitudes towards system risk using a financial risk management measure by controlling the variability of the recourse cost. The developed method is applied to irrigation water resources allocation for the Zhangweinan River Basin in China. Solutions of irrigation water allocation under different probability distributions and confidence levels are generated. Results reveal that system benefit and satisfaction degree would decrease with the raised λ levels; the CVaR values would change with the increment of λ and β levels as well as the varied cases; the irrigation targets of all crops in all subareas are reach their lower bounds; the irrigation deficits would be different with risk levels and cases. The results can help the local authority to adjust the current food security policy.

Risk aversion based interval stochastic programming approach for agricultural water management under uncertainty

In this study, a risk aversion based interval stochastic programming (RAIS) method is proposed through integrating interval multistage stochastic programming and conditional value at risk (CVaR) measure for tackling uncertainties expressed as probability distributions and intervals within a multistage context. The RAIS method can reflect dynamic features of the system conditions through transactions at discrete points in time over the planning horizon. Using the CVaR measure, RAIS can effectively reflect system risk resulted from random parameters. When random events are occurred, the adjustable alternatives can be achieved by setting desired targets according to the CVaR, which could make the revised decisions to minimize the economic penalties. Then, the RAIS method is applied to planning agricultural water management in the Zhangweinan River Basin that is plagued by drought due to serious water scarcity. A set of decision alternatives with different combinations of risk levels employed to the objective function and constraints are generated for planning water resources allocation. The results can not only help decision makers examine potential interactions between risks under uncertainty, but also help generate desired policies for agricultural water management with a maximized payoff and a minimized loss.

Taguchi-factorial type-2 fuzzy random optimization model for planning conjunctive water management with compound uncertainties

In this study, a type-2 fuzzy random optimization (TFRO) method is developed for planning conjunctive water management system associated with compound uncertainties. TFRO can effectively address compound uncertainties expressed as type-2 fuzzy sets, probability distributions, and type-2 fuzzy random variables. Solution algorithm based on the degree of probability and the information of plausibility is proposed to transform nonlinear objective function and constraints into their linear equivalents. A real case of water-resources allocation problem in Zhangweinan River Basin (China) is employed to demonstrate the applicability of the proposed method. A Taguchi-factorial type-2 fuzzy random model is also formulated through introducing Taguchi design and ANOVA technique into the TFRO framework. Results obtained can help reveal the relationship among multiple impact factors of economic, environmental and resource (water conveyance efficiency, water delivery cost, and system violation risk), as well as quantify their contributions to the variability of system benefit and water allocation schemes.

Assessment of uncertainty effects on crop planning and irrigation water supply using a Monte Carlo simulation based dual-interval stochastic programming method

In this study, a Monte Carlo simulation based dual-interval stochastic programming (MC-DSP) method is developed for assessment of uncertainty effects on crop planning and irrigation water supply associated with multiple uncertainties expressed as dual intervals and probability distributions. MC-DSP can permit in-depth analyses of various policies that are associated with different levels of economic consequences (due to uncertain water inflow) when the pre-regulated irrigation targets are violated. The developed method is applied to crop planning and water allocation for the Zhangweinan River Basin in China. Solutions of crop planning and irrigation-water allocation under different probability distributions and plausibility degrees are generated. Results reveal that surface water availabilities associated with different probability distributions can lead to changed system benefits and irrigation shortages. Moreover, water is insufficient to satisfy the requirement for wheat due to its high requirement for irrigation, which may lead to the risk of food supply. Each subarea of farmland would suffer water deficit under all scenarios (particularly for subareas of Daming county and Neihuang county) when inflow level range from very-low to high. The conflicts between economic development and agricultural sustainability would be a challenged issue that would enforce the local authority to adjust the current food security policy.

A type-2 fuzzy interval programming approach for conjunctive use of surface water and groundwater under uncertainty

In this study, a type-2 fuzzy interval programming (T2FIP) method is developed for conjunctive use of surface water and groundwater under uncertainty. T2FIP can effectively tackle uncertainties expressed as a hybrid of type-2 fuzzy sets and interval numbers. In T2FIP, tradeoff between system benefit and system reliability can be analyzed to obtain the practical modeling outputs. Solution method based on interactive algorithm and type reduction technique is proposed to transform fuzzy-interval constraint into its deterministic equivalents. The T2FIP method is then applied to conjunctive use of surface water and groundwater in the Zhangweinan River Basin, China. Scenarios associated with different groundwater utilization ratios are examined to help generate the optimal conjunctive water use pattern. Results show that increased groundwater utilization ratio could lead to increased crop area and system benefit; however, when the utilization ratio of groundwater for irrigation increases to 50%, the system benefit would not increase. Results reveal that, for arid and semi-arid regions, effective conjunctive use of surface water and groundwater is critical for guaranteeing the agricultural sustainability. These findings can help identify desired decision alternatives among crop planning, agricultural irrigation, and groundwater utilization with a maximized system benefit and a minimized system-failure risk.

An inexact risk management model for agricultural land-use planning under water shortage

Water resources availability has a significant impact on agricultural land-use planning, especially in a water shortage area such as North China. The random nature of available water resources and other uncertainties in an agricultural system present risk for land-use planning and may lead to undesirable decisions or potential economic loss. In this study, an inexact risk management model (IRM) was developed for supporting agricultural land-use planning and risk analysis under water shortage. The IRM model was formulated through incorporating a conditional value-at-risk (CVaR) constraint into an inexact two-stage stochastic programming (ITSP) framework, and could be used to control uncertainties expressed as not only probability distributions but also as discrete intervals. The measure of risk about the second-stage penalty cost was incorporated into the model so that the trade-off between system benefit and extreme expected loss could be analyzed. The developed model was applied to a case study in the Zhangweinan River Basin, a typical agricultural region facing serious water shortage in North China. Solutions of the IRM model showed that the obtained first-stage land-use target values could be used to reflect decision-makers’ opinions on the long-term development plan. The confidence level α and maximum acceptable risk loss β could be used to reflect decisionmakers’ preference towards system benefit and risk control. The results indicated that the IRM model was useful for reflecting the decision-makers’ attitudes toward risk aversion and could help seek cost-effective agricultural land-use planning strategies under complex uncertainties.

Double-sided fuzzy chance-constrained linear fractional programming approach for water resources management

A double-sided fuzzy chance-constrained fractional programming (DFCFP) method is developed for planning water resources management under uncertainty. In DFCFP the system marginal benefit per unit of input under uncertainty can also be balanced. The DFCFP is applied to a real case of water resources management in the Zhangweinan River Basin, China. The results show that the amounts of water allocated to the two cities (Anyang and Handan) would be different under minimum and maximum reliability degrees. It was found that the marginal benefit of the system solved by DFCFP is bigger than the system benefit under the minimum and maximum reliability degrees, which not only improve economic efficiency in the mass, but also remedy water deficiency. Compared with the traditional double-sided fuzzy chance-constrained programming (DFCP) method, the solutions obtained from DFCFP are significantly higher, and the DFCFP has advantages in water conservation.

Planning an Agricultural Water Resources Management System: A Two-Stage Stochastic Fractional Programming Model

Irrigation water management is crucial for agricultural production and livelihood security in many regions and countries throughout the world. In this study, a two-stage stochastic fractional programming (TSFP) method is developed for planning an agricultural water resources management system under uncertainty. TSFP can provide an effective linkage between conflicting economic benefits and the associated penalties; it can also balance conflicting objectives and maximize the system marginal benefit with per unit of input under uncertainty. The developed TSFP method is applied to a real case of agricultural water resources management of the Zhangweinan River Basin China, which is one of the main food and cotton producing regions in north China and faces serious water shortage. The results demonstrate that the TSFP model is advantageous in balancing conflicting objectives and reflecting complicated relationships among multiple system factors. Results also indicate that, under the optimized irrigation target, the optimized water allocation rate of Minyou Channel and Zhangnan Channel are 57.3% and 42.7%, respectively, which adapts the changes in the actual agricultural water resources management problem. Compared with the inexact two-stage water management (ITSP) method, TSFP could more effectively address the sustainable water management problem, provide more information regarding tradeoffs between multiple input factors and system benefits, and help the water managers maintain sustainable water resources development of the Zhangweinan River Basin.

A dual-interval fixed-mix stochastic programming method for water resources management under uncertainty

In this study, a dual-interval fixed-mix stochastic programming (DFSP) method is developed for planning water resources management systems under uncertainty. DFSP incorporates interval-parameter programming (IPP) and fuzzy vertex analysis (FVA) within a fixed-mix stochastic programming (FSP) framework to address uncertain parameters described as probability distributions and dual intervals. It can also be used for analyzing various policy scenarios that are associated with different levels of economic consequences since penalties are exercised with recourse actions against any infeasibility. A real case for water resources management planning of Zhangweinan River Basin in China is then conducted for demonstrating the applicability of the developed DFSP method. Solutions in association with α-cut levels are generated by solving a set of deterministic submodels, which are useful for generating a range of decision alternatives under compound uncertainties. The results can help to identify desired water-allocation schemes for local sustainable development that the prerequisite water demand can be guaranteed when the available water resource is scarce.

Optimization Model for Planning Regional Water Resource Systems under Uncertainty

This study proposes an interval semiinfinite De Novo programming (ISIDP) method for the planning of water resource management systems under uncertainty. The ISIDP problem is settled by dividing it into two interactive linear programming subproblems and solving it by using a conventional simplex method. To evaluate the applicability of the proposed model, the method is applied to a case study of the Yuecheng Reservoir in Zhangweinan River Basin, China. The results indicate that the strategies generated through ISIDP would not increase the complexity in decision-making processes. Compared with the conventional optimization method, ISIDP has the advantages of (1) better reflecting the association of the system benefits with water price, (2) generating more reliable solutions with a lower risk of system failure as a result of the possible violation of constraints, and (3) providing more flexible management planning because the availability of budgets can be adjusted with the variations in water price.

A hybrid fuzzy-stochastic programming method for water trading within an agricultural system

In this study, a hybrid fuzzy-stochastic programming method is developed for planning water trading under uncertainties of randomness and fuzziness. The method can deal with recourse water allocation problems generated by randomness in water availability and, at the same time, tackle uncertainties expressed as fuzzy sets in the trading system. The developed method is applied to a water trading program within an agricultural system in the Zhangweinan River Basin, China. Results can reflect the decisions for water allocation and crop irrigation under various flow levels; this allows corrective actions to be taken based on the predefined policies for cropping patterns and can thus help minimize the penalty due to water deficit. The results indicate that trading can release excess water while still keeping the same agricultural revenue obtained in a non-trading scheme. This implies that trading scheme is effective for obtaining high economic benefit, particularly for one water-resources scarcity region. Results also indicate that the effectiveness of the trading program is explicitly affected by uncertainties expressed as randomness and fuzziness, which challenges the users to make decisions of their water demands due to uncertain water availability. Sensitivity analysis is also conducted to analyze the impacts of trading costs, demonstrating that the trading efforts could become ineffective when the trading costs are too high.

A multistage irrigation water allocation model for agricultural land-use planning under uncertainty

In practical agricultural water management problems, fluctuating water availabilities and demands, varying crop yields and economic profits, as well as changing irrigation patterns in both temporal and spatial scales are challenged decision makers. These challenges are being further compounded by rapid socioeconomic development associated with increased food requirement and decreased resources accessibility. A multistage irrigation water allocation (MIWA) model is developed for agricultural water management and cropland use planning in response to such complexities. The MIWA model is derived from incorporating interval parameters within a multistage stochastic programming (MSP) framework, such that uncertainties expressed as interval parameters and probability distributions can be tackled, and the real-time dynamic irrigation water management can be conducted. It can also support the analysis of various policy scenarios that are associated with different levels of economic consequences when the pre-regulated crop targets are violated over a multistage context. The MIWA model is then applied to a real case of planning agricultural water management and cropland use pattern in Zhangweinan River Basin, which is one of the driest regions in China and faces serious water scarcity. Solutions of irrigation targets for multiple crops as well as actual water-allocation patterns in different growth stages can help determine optimized water and land use in agricultural system, which could hedge appropriately against future available water levels in more profitable and sustainable ways.

Risk assessment of agricultural irrigation water under interval functions

In recent years, water shortages and unreliable water supplies have been considered as major barriers to agricultural irrigation water management in China, which are threatening human health, impairing prospects for agriculture and jeopardizing survival of ecosystems. Therefore, effective and efficient risk assessment of agricultural irrigation water management is desired. In this study, an inexact full-infinite two-stage stochastic programming (IFTSP) method is developed. It incorporates the concepts of interval-parameter programming and full-infinite programming within a two-stage stochastic programming framework. IFTSP can explicitly address uncertainties presented as crisp intervals, probability distributions and functional intervals. The developed model is then applied to Zhangweinan river basin for demonstrating its applicability. Results from the case study indicate that compromise solutions have been obtained. They provide the desired agricultural irrigation water-supply schemes, which are related to a variety of tradeoffs between conflicting economic benefits and associated penalties attributed to the violation of predefined policies. The solutions can be used for generating decision alternatives and thus help decision makers to identify desired agricultural irrigation targets with maximized system benefit and minimized system-failure risk. Decision makers can adjust the existing agricultural irrigation patterns, and coordinate the conflict interactions among economic benefit, system efficiency, and agricultural irrigation under uncertainty.

Agricultural Water Management under Uncertainty Using Minimax Relative Regret Analysis Method

AbstractIn this study, an inexact minimax relative regret analysis (IMRA) method is proposed for planning the agricultural water management under uncertainty. The IMRA incorporates interval-parameter programming (IPP) and minimax relative regret analysis (MRA) within an integer programming framework. IMRA can deal with uncertainties expressed as both interval values and random variables; it is capable of seeking an optimal strategy through analyzing all possible scenarios without any assumption on their probabilities. The IMRA method is then applied to a real case of agricultural water management in the Zhangweinan River Basin, one of the main food and cotton producing regions in North China and facing severe water shortage. An interval relative regret matrix is obtained based on a matrix of interval-element system benefit, and it is helpful for decision makers to identify the optimal irrigation strategy with the maximum system benefit and the minimum worst relative regret.

Two-Stage Inexact-Probabilistic Programming Model for Water Quality Management

Abstract A two-stage inexact-probabilistic programming (TIPP) method was developed for water quality management in Zhangweinan River Basin, through coupling two-stage stochastic programming with inexact chance-constrained programming. The developed TIPP can handle uncertainties presented as both discrete intervals and probability distributions, and can support the assessment of the reliability of satisfying (or the risk of violating) system's constraints for accomplishing a maximized system benefit. It can also be used for analyzing policy scenarios that are associated with different levels of economic penalties when the promised targets are violated. Interval solutions under different constraint-violation levels have been obtained. They can be used for generating decision alternatives and thus help managers to identify desired policies under various environmental, economic, and system-reliability constraints.

Assessment and Spatiotemporal Variation Analysis of Water Quality in the Zhangweinan River Basin, China

Abstract Spatiotemporal variation analysis of water quality and identification of water pollution sources in river basins is very important for water resources protection and sustainable utilization. In this study, fuzzy comprehensive analysis and two statistical methods including cluster analysis and seasonal Kendall test method were used to evaluate the spatiotemporal variation of water quality in the Zhangweinan River basin. The results for spatial cluster analysis and assessment on water quality at 19 monitoring sites indicated that water quality in the Zhangweinan River basin could be classified into two regions according to pollution levels. One is the Zhang River basin located in northwest of the Zhangweinan River basin where water quality is good. Another one includes the Wei River and eastern plain of the Zhangweinan River basin, and the water pollution in this region is serious, where the pollutants from point sources flow into the river and the water quality changes greatly. The results of temporal cluster analysis and seasonal Kendall test indicated that the sampling periods may be classified into three periods during 2002-2009 according to water quality. Results of temporal cluster analysis and seasonal Kendall test indicated that the study periods may be classified into three periods and two different trends was detected during the period of 2002-2009. The first period was the year of 2002-2003, during which water quality had deteriorated and serious pollution was observed in the Wei River basin and eastern plain of the Zhangweinan River basin. The second period was the year of 2004-2006, during which water quality became better. The year of 2007-2009 is the third period, during which water quality had been improved greatly. Despite that water quality in the Zhangweinan River basin had been improved during the period of 2004-2009, water quality in the Wei River (southwestern part of the basin), the Wei Canal River and the Zhangweixin River (eastern plain of the basin) is still poor. These results provide may useful information for better pollution control strategies in the Zhangweinan River basin.

Trends in Temperature and Precipitation in the Zhangweinan River Basin during the last 53 Years

Abstract Based on monthly mean temperature and precipitation data for 53 years, from 1957 to 2009, recorded at the Zhangweinan River Basin and five main meteorological stations around it, the non-parametric Mann-Kendall trend test method and cumulative anomaly methods were adopted to analyze the long-term trends here of such key meteorological elements as annual mean temperature, maximum temperature, minimum temperature and precipitation; the corresponding Hurst index was calculated; and future change trends for various meteorological elements were predicted. The results of the analyses indicate that: there is an inconspicuous downward trend in annual precipitation for the Zhangweinan River Basin, with large decreases appearing in summer, with an M-K slope of -11.82 mm/10a; the annual mean temperature rises more significantly, and the M-K slope of the whole Basin's annual mean temperature reaches 0.17°C/10a, the temperature increase being significant in spring and winter. According to the Hurst Index calculations, the annual mean temperature and precipitation series show strong continuity in the Zhangweinan River Basin, which indicates that the future trend will be consistent with that of the past. These research results will not only help provide evidence for the climate change in the Zhangweinan River Basin but can also lay the foundation for further studies on water resources security evaluation in conditions of climate change.

Planning Agricultural Water Resources System Associated With Fuzzy and Random Features1

Li, Y.P. and G.H. Huang, 2011. Planning Agricultural Water Resources System Associated With Fuzzy and Random Features. Journal of the American Water Resources Association (JAWRA) 47(4):841-860. DOI: 10.1111/j.1752-1688.2011.00558.x Abstract: More and more regions where demand outstrips water resources availability have suffered from chronic severe shortages. It is particularly aggravated for agricultural irrigation systems where more water is necessary to support the rapidly increasing population and speedily developing economy. In this study, a two-stage fuzzy-stochastic programming (TFSP) method is developed for planning agricultural water resources management system in more efficient and sustainable ways. The developed method can address uncertain parameters described as probability distributions and fuzzy sets. It can also be used for analyzing various policy scenarios that are associated with different levels of economic consequences since penalties are exercised with recourse actions against any infeasibility. The developed method is applied to agricultural water-resources management planning of the Zhangweinan River Basin, China. Solutions under various α-cut levels and fuzzy dominance indices can be generated by solving a series of deterministic submodels, which can help determine optimized crop-target values that could hedge appropriately against future available water levels. The results are helpful for water resources managers in not only making decisions of crop irrigation but also gaining insight into the tradeoffs between economic objective and system-failure risk.

Trans-jurisdictional pollution control options within an integrated water resources management framework in water-scarce north-eastern China

The extent and severity of water pollution in China is well known, as is the fact that until the 11th Five Year Plan (FYP) in 2006, much greater importance was placed on economic growth than environmental protection. There were few incentives to reduce pollution owing to an inadequate legal framework, the absence of economic measures for pollution control in favour of a command-and-control approach, and weak enforcement. Passing through four provinces and Tianjin Municipality, Zhangweinan River (Canal) Basin, part of the water-scarce Haihe River system in north-eastern China, provides an example of the types of trans-jurisdictional water pollution disputes that are common throughout China owing to inadequate application of integrated water resources management (IWRM) principles. The Zhangweinan River has a decreasing flow downstream and virtually zero assimilation capacity owing to waste loads that are vastly larger than the assimilation capacity of the river system. The fact that these trans-jurisdictional issues keep arising and, for the most part, are never resolved, reflects the failure of IWRM governance in this basin. We explore legal, institutional, planning, technical and market measures that would greatly reduce trans-jurisdictional disputes and contribute to successful IWRM in China.