Optimal Water Allocation Research Articles

Impact of water price reform on water conservation and economic growth in China

Water pricing has been used as an effective method of conserving water and optimizing water allocation. However, little is known about how to set a rational and efficient water price and how water pricing impacts economic growth. In this paper, we address this challenge by using the SICGE model, a dynamic general equilibrium model for China that is augmented by a total water constraint module. We also include a water subdivision module that allows for substitution between various water sources. These extensions facilitate a comprehensive estimate of the impact that various water price reforms have on water conservation and economic growth. The modeling results confirm that an increase in the water price will lead to a decline in total water usage, a better water use structure, and enhanced water use efficiency. We conclude with a comparison of multiple scenarios that suggests an optimal water price system.

An uncertainty-based framework for agricultural water-land resources allocation and risk evaluation

Agricultural land and water resources are simultaneously declining due to population growth and economic expansion, which emphasizes the need for optimal allocation of these resources to balance socioeconomic development and water conservation. This study develops a framework for allocation of agricultural land-water resources and risk evaluation under uncertainty. The framework is capable of fully reflecting multiple uncertainties expressed as intervals and probability distributions, considering the connections of agricultural water and land resources. The developed framework will be helpful for managers in gaining insights into the tradeoffs between system benefits and constraint-violation risks, permitting an in-depth analysis of risks of agricultural irrigation water shortage under various violating probabilities. The framework is applied for optimization of agricultural water and land resources in the middle reaches of Heihe River basin. A series of water and land allocation results under different flow levels and violating probabilities were obtained and analyzed in detail through optimally allocating limited water and land resources to different irrigation areas and crops. Comparison with actual conditions shows that both the “net benefit per unit water” and “net benefit per unit land” increase which will demonstrate the feasibility and applicability of the developed framework. In addition, probability distributions of water allocation under various flow levels are generated to help decision makers learn detailed water distribution information and thus help make comprehensive irrigation schemes in the planning horizon under uncertainty. Results of evaluation of agricultural irrigation water shortage risks indicate that the water shortage risks in the middle reaches of Heihe River basin are in the category of acceptable risk level or brink risk level. The developed framework can be valuable for providing a reliable decision aid for optimal water and land resources allocation, and can ensure that the management policies and plans are made with reasonable consideration of both system benefits and risks.

Erratum for “Improved Ant Colony Optimization for Optimal Crop and Irrigation Water Allocation by Incorporating Domain Knowledge” by D. C. H. Nguyen, G. C. Dandy, H. R. Maier, and J. C. Ascough II

Erratum for “Improved Ant Colony Optimization for Optimal Crop and Irrigation Water Allocation by Incorporating Domain Knowledge” by D. C. H. Nguyen, G. C. Dandy, H. R. Maier, and J. C. Ascough II

Real-time optimal water allocation for daily hydropower generation from the Vanderkloof dam, South Africa

The adverse effects of power shortages resulting from escalating energy demands, due to rapid global urbanization and industrial developments, have driven efforts worldwide in search for improved techniques for sustainable reservoir operations and optimization for hydropower generation. Recent studies have shown that, combining accurate reservoir inflow forecasting procedures with efficient optimization techniques can produce more efficient and balanced solutions, for operation of multipurpose reservoir systems to improve on the economy of hydropower production. This study presents the coupling of a data driven artificial neural network (ANN) model and a novel combined Pareto multi-objective differential evolution (CPMDE), for hydrological simulation and multi-objective numerical optimization of hydropower production, from the Vanderkloof dam in real-time. Results from the application of the real-time strategy, indicate a significant improvement in performance over the current practice. Therefore, the hybrid ANN-CPMDE real-time reservoir operation model suggested herein provides a low cost solution methodology, suitable for sustainable operation of the Vanderkloof reservoir in South Africa.

Improving agricultural water productivity to ensure food security in China under changing environment: From research to practice

Irrigation is an important measure for increasing grain production. Improving water use efficiency in agriculture is expected to play a very important role in ensuring food and water security in China, since there is a serious problem between food supply and limited water resources in China. The present state and future trend of water and food security in China were analyzed, while the importance of irrigation in ensuring China food security was highlighted based on the analysis of the evolution of irrigation water productivity in recent 60 years and its relationships with changes of crop yield, cropping pattern, fertilization and irrigation water use. Research progresses and practical application on high-efficient agricultural water use in China were introduced, and two successful cases of improving agricultural water productivity in China were presented, one was to improve crop water use efficiency by the novel irrigation method based on crop physiological responses, and the other was to improve the regional water productivity by the integrative methods in the Shiyang River Basin of Northwest China. The major research areas needed to focus on in the future were discussed, which include responses of crop water demand to changing environment and associated spatio-temporal optimization of water allocation, multi-processes hydrologic cycle of irrigated land under strong influences of human activities, integrated measures for improving multi-scale agricultural water use efficiency, and interactions between grain production, water resources and ecological system and its sustainability analysis in a systematic way.

Optimization Model for the Sustainable Water Resource Management of River Basins

This paper presents a new methodology for the sustainable and optimal allocation of water for a river basin management area. The model distinguishes between short and long-term planning horizons and goals using a short-term modeling component (STM) and a long term modeling component (LTM) respectively. An STM is a linear programming problem, and optimizes a monthly allocation schedule on an annual basis in terms of maximum net economic benefit. A cost of depletion is included in the STM net benefit calculation to address the non-use value of groundwater. An LTM consists of an STM for every year of the long-term planning horizon. Each LTM is quantified using a sustainability index, with sustainability defined in terms of risk to supply and ecological, environmental, and hydrological integrity. The LTMs are optimized to determine the most sustainable net economic benefit for the management area using a genetic algorithm. The model is applied to a test case representative of a management area in Arizona in order to highlight its potential utility.

Optimal spatial allocation of irrigation water under uncertainty using the bilayer nested optimisation algorithm and geospatial technology

ABSTRACTThe optimal spatial allocation of irrigation water under uncertainty has become a serious concern because of irrigation water shortage and uncertain factors that affect irrigation water allocation. In this study, an optimal multi-objective model for irrigation water allocation under uncertainty is developed to maximise the economic benefit of crops and minimise the operation cost and water deficit of crop irrigation. The original and optimal plantation structure, irrigation mode and soil water content are acquired through geospatial technology. A bilayer nested optimisation (BLNO) algorithm is designed to produce multiple individuals of design vectors using an ant colony neural network algorithm for an outer optimisation. Meanwhile, a continuous adaptive ant colony (CAAC) algorithm is used for inner optimisation to calculate the interval values of the uncertain model. The crop distribution and irrigation mode are obtained to parameterise the planting area and the water demand of each crop and each block in the multi-objective model. This model is then solved and analysed. Compared to the optimal schemes obtained from an inexact two-stage fuzzy-stochastic programming and the CAAC, respectively, BLNO can effectively and efficiently solve the optimal spatial allocation of irrigation water under uncertainty. This method can spatially maximise the economic benefit of crops and minimise the operation cost and water deficit of crop irrigation using lower and upper bound maps whilst visually obtaining the exact crop type, reasonable irrigation method and precise water demand for each block and for the entire irrigated area.

Application of Integrated Hydrologic and River Basin Management Modeling for the Optimal Development of a Multi-Purpose Reservoir Project

Multi-purpose reservoir development have been always a big challenge for the management of water resources. This paper describes an integrated approach for investigating catchment hydrology in the development of a hydropower and a canal irrigation system based on model analyses. The investigation aims to adequately determine an optimal domestic and irrigation water resources allocation scheme based on an assessment of the reservoir water balance and capacity for hydropower. The soil and water assessment tool (SWAT) which characterizes basin hydrology and the water management and planning model MODSIM which provides a decision support system for water allocation optimization, were used in this study. The integrated approach was applied to Prek Te River basin in Cambodia. The water demand aspect was examined based on domestic water use, irrigation water, environmental flow, and water losses. An operational rule curve was developed for hydropower operation with respect to a power potential of 13 MW. Hydrologic modeling revealed 90 % dependable water of about 2.7 m3/s during the dry season and 214.3 m3/s during the wet season, indicative of a wet-season dependent reservoir for storage. Results from the 26-years simulation period also showed that diversions for domestic water and irrigation water supply were 92.3 % dependable for a 13 MW capacity hydropower development. The integrated approach was shown to be a valuable decision support tool for water resources management with the determination of an optimum policy for multi-purpose reservoir operation based on available basin water supply.

Crop Water Production Functions—A Review of Available Mathematical Method

<p>Crop water production function is the relationship between obtained marketable yield and the total amount of water used by plant through evapotranspiration. Different mathematical expressions have been derived from field experiments to predict yield knowing water use. Such expressions provide a powerful tool in water allocation optimization. However these functions are commonly confused with the relationship between available water and crop yield. This paper reviews functions that describe crop water production, the context they can be used for, and their strength and weakness.</p>

Comparing the efficiency of GA and PSO metaheuristic algorithms in optimal allocation of water to agricultural farms in water scarcity condition

Comparing the efficiency of GA and PSO metaheuristic algorithms in optimal allocation of water to agricultural farms in water scarcity condition

Farm-based cropping pattern optimization and conjunctive use planning using piece-wise genetic algorithm (PWGA): a case study

So far, several agro-economic optimization models have been developed in order to deal with agricultural water resources planning and management, among which the cropping pattern and water allocation optimization models are of significant importance. However, these models usually consider the land as a plain entirety and fail to account for a number of important contributing factors, including the private ownership of agricultural lands and the unique characteristics of each land parcel. Hence, the solutions provided by these models are not fully applicable in real-world and are only meant to help the decision-maker in analyses. In the present study, a new model is developed in order to incorporate farm-level data, and to propose optimal cropping pattern and conjunctive use decisions at farm level. Furthermore, a modified genetic algorithm has been developed by piecewise reorganization of chromosome structure, namely piecewise genetic algorithm (PWGA), and is presented in order to tackle the nonlinearity and the high number of variables involved in the model. A URM-based groundwater model is also coupled with PWGA in order to improve the computational efficiency of the framework. The proposed model is then run for various scenarios and the results are compared with those of the general approaches. The results of this study demonstrate that the general plain-level models tend to overestimate the expected net benefits. Moreover, the proposed model is able to optimize the cropping pattern and water allocation rules over the area while supporting each land parcel with appropriate decisions.

Determination of optimal and and water allocation under limited water resources using soil water balance in Ordibehesht canal of Doroodzan water district

ABSTRACT- Inadequate water supply is the major problem for agriculture in arid and semi-arid regions. Thus, effective management should be considered for water resources planning. In this research, a model was provided which is able to estimate optimal land and water allocation in the Doroodzan irrigation network. Optimal water management model was used at farm level to evaluate different deficit irrigation (DI) strategies at various periods of crop growth. Genetic algorithm toolbox by MATLAB (Mathworks, 2009) software was used for benefit optimization considering practical constraints. Results showed that deficit irrigation technique significantly reduced water allocation and increased the crops cultivation area in the region. In addition, increase in water price and the occurrence of drought resulted in cropping pattern change and led to including crops with high economic values. Application of this model ensures the optimal use of available water resources in all conditions, especially under drought condition. The proposed model is capable of defining water management plan with regard to the amount of available water and price of water and product, for simultaneous optimal land and water allocation.

Optimal Operation of Water Distribution Networks under Water Shortage Considering Water Quality

AbstractWater shortages are caused by hydrological droughts and by the disruption of the operation of water distribution networks (WDNs). The water pressure and residual chlorine concentration are examples of quantitative and qualitative indexes, respectively, of a WDN’s performance. This work considers quality and quantity variables simultaneously in the operation of WDNs under water shortages. An optimization model is developed to find the optimum water allocation schedule in WDNs. The objectives of the optimization model are maximizing the number of node-times in which the chlorine concentration is in the allowable range, and maximizing the number of supply nodes under desirable pressure. These objectives satisfy the principle of justice in water distribution under water shortage. The optimization model was solved for a real WDN under different scenarios using the honey-bee mating optimization (HBMO) algorithm linked to a hydraulic simulator. The performance of the developed model was compared to an op...

Modelling Intelligent Water Resources Allocation for Multi-users

This study proposes intelligent water resources allocation strategies for multiple users through hybrid artificial intelligence techniques implemented for reservoir operation optimization and water shortage rate estimation. A two-fold scheme is developed for (1) knowledge acquisition through searching input–output patterns of optimal reservoir operation by optimization methods and (2) the inference system through mapping the current input pattern to estimate the water shortage rate by artificial neural networks (ANNs). The Shihmen Reservoir in northern Taiwan is the study case. We first design nine possible water demand conditions by investigating the changes in historical water supply. With the nine designed conditions and 44-year historical 10-day reservoir inflow data collected during the growth season (3 months) of the first paddy crop, we first conduct the optimization search of reservoir operation by using the non-dominated sorting genetic algorithm-II (NSGA-II) in consideration of agricultural and public water demands simultaneously. The simulation method is used as a comparative model to the NSGA-II. Results demonstrate that the NSGA-II can suitably search the optimal water allocation series and obtain much lower seasonal water shortage rates than those of the simulation method. Then seasonal water shortage rates in response to future water demands for both sectors are estimated by using the adaptive network fuzzy inference system (ANFIS). The back-propagation neural network (BPNN) is adopted as a comparative model to the ANFIS. During model construction, future water demands, predicted monthly inflows (or seasonal inflow) of the reservoir in the next coming quarter and historical initial reservoir storages configure the input patterns while the optimal seasonal water shortage rates obtained from the NSGA-II serve as output targets (training targets) for both neural networks. Results indicate that the ANFIS and the BPNN models produce almost equally good performance in estimating water shortage rates, yet the ANFIS model produces even better stability. The reliability of the proposed scheme is further examined by scenario analysis. The scenario analysis indicates that an increase in public water demand or a decrease in agricultural water demand would bring more impacts of water supply on agricultural sectors than public sectors. Similarly, a bigger decrease in inflow amount would obviously bring more influence on agricultural sectors than public one. Consequently, given predicted inflow, decision makers can pre-experience the possible outcomes in response to competing water demands through the estimation models in order to determine adequate water supply as well as preparedness measures, if needed, for drought mitigation.

Changes in Grain Production and the Optimal Spatial Allocation of Water Resources in China

Abstract: Changes in grain production are decomposed and compared among nine major Chinese river basins for the sake of optimal water allocation. The results show that water-deficient northern China, especially the Songliao River Basin and Huai River Basin, contributed the greatest share of the total grain increment from 1995 to 2010. The Songliao River Basin achieved increased grain output largely by expanding multiple cropping, while the Huai River Basin achieved it mainly by improving the yield per unit area. With increased reliance on expanding irrigation and multi-cropping, most northern basins have high levels of agricultural water consumption, despite the rising share of corn, a lower water intensive crop. In contrast, over the same period the warm and humid south, traditionally a major rice-growing area, mostly experienced a sharp decline in rice cropping area and the Southeast Rivers Basin even reduced multiple cropping indexes, contributing to decreased agricultural water consumption. Implicatio...

Framework for computationally efficient optimal crop and water allocation using ant colony optimization

A general optimization framework is introduced with the overall goal of reducing search space size and increasing the computational efficiency of evolutionary algorithm application to optimal crop and water allocation. The framework achieves this goal by representing the problem in the form of a decision tree, including dynamic decision variable option (DDVO) adjustment during the optimization process and using ant colony optimization (ACO) as the optimization engine. A case study from literature is considered to evaluate the utility of the framework. The results indicate that the proposed ACO-DDVO approach is able to find better solutions than those previously identified using linear programming. Furthermore, ACO-DDVO consistently outperforms an ACO algorithm using static decision variable options and penalty functions in terms of solution quality and computational efficiency. The considerable reduction in computational effort achieved by ACO-DDVO should be a major advantage in the optimization of real-world problems using complex crop simulation models.

Social resolution of conflicts over water resources allocation in a river basin using cooperative game theory approaches: a case study

ABSTRACTIn this paper, cooperative game theory (CGT) approaches are used for water allocation in a river basin considering equity benefit shares among stakeholders. An optimization model is initially developed for allocating water to competing users including agricultural, industrial, and environmental users based on both economic objectives. The model is implemented to determine water shares for different likely coalitions among water users. Then, CGT approaches such as Shapely, Nucleolus, and Nash-Harsanyi are used for reallocating net profits to the users as an attempt to encourage them to participate in equitable cooperation. Finally, the results of different game theoretic approaches are evaluated using the stability index and voting methods such as social choice and fallback bargaining. The proposed methodology is applied to the Zayandehrood River basin located in central part of Iran which struggles with water scarcity. The different CGT approaches applied to two predefined real-life scenarios in the basin under study and their performance were evaluated. The results indicate the proper performance of both Nash-Harsanyi and Shapely methods for pessimist and optimistic scenarios, respectively. It is also found that application of the proposed methodology effectively increases the users’ benefits in the study region through optimal water allocation and reallocation of benefits.

Multi-objective Linear Programming for Optimal Water Allocation Based on Satisfaction and Economic Criterion

In this study, a simple deterministic water allocation model was developed to optimally allocate limited available water resources among different water-use sectors. The model considers two single-objective functions and one multi-objective function. The first single objective (B0W1) optimizes the satisfaction levels among various water demand sectors, whereas the second single objective (B1W0) maximizes net economic benefits. The multi-objective (B1W1) combines the first two single objectives. For the multiobjective function, the model considers two optimization techniques, a simultaneous compromise constraint technique and a weighting technique to optimize both the satisfaction level and economic benefits. The model is applied to the Hingol River basin in the Baluchistan Province of Pakistan. To evaluate the model’s applicability under different situations, different schemes are applied to consider variations in the minimum satisfaction level and to assign priorities to various water-use sectors. The results indicate that the value of economic benefits obtained by B1W1 lies between B0W1 and B1W0. This is a compromise between the two individual objectives. The model is easy to adopt under different conditions, because of its simplicity and flexibility.

Adaptive meta-modeling-based simulation optimization in basin-scale optimum water allocation: a comparative analysis of meta-models

Incorporating river basin simulation models in heuristic optimization algorithms can help modelers address complex, basin-scale water resource problems. We have developed a hybrid optimization-simulation model by linking a stretching particle swarm optimization (SPSO) algorithm and the MODSIM river basin decision support system (DSS), and have used the SPSO-MODSIM model to optimize water allocation at basin scale. Due to high computational cost of the SPSO-MODSIM model, we have, subsequently, used four meta-model types of artificial neural networks (ANN), support vector machines (SVM), kriging and polynomial response functions, replacing the MODSIM DSS, in an adaptively learning meta-modeling approach. The performances of the meta-models are first compared in two Ackley and Dejong benchmark functions optimization problems, and the meta-models are then evaluated by solving the Atrak river basin water allocation optimization problem in Iran. The results demonstrate that independent of the meta-model type, the sequentially space-filling meta-modeling approach can improve the performance of meta-models in the course of optimization by adaptively locating the promising regions of the search space where more samples need to be generated. However, the ANN and SVM meta-models perform better than others in saving the number of costly, original objective function evaluations.

Optimizing Water Allocation under Uncertain System Conditions for Water and Agriculture Future Scenarios in Alfeios River Basin (Greece)—Part B: Fuzzy-Boundary Intervals Combined with Multi-Stage Stochastic Programming Model

Optimal water allocation within a river basin still remains a great modeling challenge for engineers due to various hydrosystem complexities, parameter uncertainties and their interactions. Conventional deterministic optimization approaches have given their place to stochastic, fuzzy and interval-parameter programming approaches and their hybrid combinations for overcoming these difficulties. In many countries, including Mediterranean countries, water resources management is characterized by uncertain, imprecise and limited data because of the absence of permanent measuring systems, inefficient river monitoring and fragmentation of authority responsibilities. A fuzzy-boundary-interval linear programming methodology developed by Li et al. (2010) is selected and applied in the Alfeios river basin (Greece) for optimal water allocation under uncertain system conditions. This methodology combines an ordinary multi-stage stochastic programming with uncertainties expressed as fuzzy-boundary intervals. Upper- and lower-bound solution intervals for optimized water allocation targets and probabilistic water allocations and shortages are estimated under a baseline scenario and four water and agricultural policy future scenarios for an optimistic and a pessimistic attitude of the decision makers. In this work, the uncertainty of the random water inflows is incorporated through the simultaneous generation of stochastic equal-probability hydrologic scenarios at various inflow positions instead of using a scenario-tree approach in the original methodology.