Simulation-Optimization Modeling of Conjunctive Use of Surface Water and Groundwater

Water resources management in semiarid regions with low precipitation and high potential of evapotranspiration is a great challenge for managers and decision makers. In those regions, both sources of water should be managed conjunctively so as to minimize shortages of water in dry seasons. In conjunctive use, the difficulty increases as one must represent the response of both systems interactions, and develop management strategies that simultaneously address surface water and aquifer regulation. This paper focuses on the simulation-optimization for conjunctive use of surface water and groundwater on a basin-wide scale, the Najafabad plain in west-central Iran. A trained artificial neural network model is developed as a simulator of surface water and groundwater interaction while a genetic algorithm is developed as the optimization model. The main goal of the simulation-optimization model is to minimize shortages in meeting irrigation demands for three irrigation systems subject to constraints on the control of cumulative drawdown of the underlying water table and maximum capacity of surface irrigation systems. To achieve the main goal, three scenarios are presented. Results of the proposed model demonstrate the importance of the conjunctive use approach for planning the management of water resources in semiarid regions.     

Development of Priority-Based Policies for Conjunctive Use of Surface and Groundwater

Abstract

A combined optimization-simulation approach was used to develop and evaluate the alternate priority-based policies for operation of surface and groundwater systems and is demonstrated with a case study. An optimization model was used to find optimal cropping pattern with and without the conjunctive use of surface and groundwater, as well as with and without socio-economic constraints. The optimization model, based on linear programming, maximizes the net benefit from irrigation activities subject to various physical, economical, and social constraints. A simulation model was used to evaluate the conjunctive operation of the system using the optimal cropping pattern derived from the optimization model. The developed policies have been verified with long-generated stream flow sequences. Three alternate priority-based policies differing in level of groundwater pumping and area of cultivation of rice crop have been evaluated: (1) irrigation with surface water only (Policy-1); (2) irrigation with conjunctive use of surface and groundwater, without socio-economic constraints (Policy-2); and (3) irrigation with conjunctive use operation and with socio-economic constraints (Policy-3). It was found that the use of available groundwater within three meters below ground level (Policy-2) to be optimal, and these results were used in simulation for further evaluation of policies. It was also found that the policy-3 of conjunctive use operation with a priority of 75 percent of maximum possible rice area (using groundwater available within four meters below ground level) resulted in a better scenario. Thus the conjunctive use Policy-2 and Policy-3 with 75 percent of maximum possible rice crop area can be used as better policies for the system studied.     

Mitigating Socio-Economic-Environmental Impacts During Drought Periods by Optimizing the Conjunctive Management of Water Resources

Multi-period optimization of conjunctive water management can utilize reservoirs and aquifer carry-over to alleviate drought impacts. Stakeholders’ socio-economic and environmental indices can be used to minimize the socio-economic and environmental costs associated with water shortages in drought periods. The knowledge gap here is the evaluation and inclusion of the socio-economic and environmental value of conjunctive water management in terms of its drought mitigation capability. In this paper, an integrated water quantity-quality optimization model that considers socio-economic and environmental indices is developed. The model considers and integrates reservoir and aquifer carry-over, river-aquifer interaction and water quality with stakeholders’ socio-economic indices of production, net income and labor force employment to evaluate the socio-economic and environmental value of conjunctive water management. Total dissolved solid (TDS) is used as the water quality index for environmental assessments. The model is formulated as a multi-period nonlinear optimization model, with analysis determining the optimal decisions for reservoir release and withdrawal from the river and aquifer in different months to maximize the socio-economic indices of stakeholders within the environmental constraints. The proposed model is used in Zayandehrood water resource system in Iran, which suffers from water supply and pollution problems. Model analysis results show that conjunctive water use in the Zayandehrood water basin reduces salinity by 50 % in the wetland and keeps water supply reduction during a drought under 10 % of irrigation demand.     

Distributed Simulation‐optimization Model for Conjunctive Use of Groundwater and Surface Water Under Environmental and Sustainability Restrictions

Evolving optimal management strategies are essential for the sustainable development of water resources. A coupled simulation-optimization model that links the simulation and optimization models internally through a response matrix approach is developed for the conjunctive use of groundwater and surface water in meeting irrigation water demand and municipal water supply, while ensuring groundwater sustainability and maintaining environmental flow in river. It incorporates the stream-aquifer interactions, and the aquifer response matrix is generated from a numerical groundwater model. The optimization model is solved by using MATLAB. The developed model has been applied to the Hormat-Golina valley alluvial stream-aquifer system, Ethiopia, and the optimal pumping schedules were obtained for the existing 43 wells under two different scenarios representing with and without restrictions on stream flow depletion, and satisfying the physical, operational and managerial constraints arising due to hydrological configuration, sustainability and ecological services. The study reveals that the total annual optimal pumping is reduced by 19.75?% due to restrictions on stream flow depletion. It is observed that the groundwater pumping from the aquifer has a significant effect on the stream flow depletion and the optimal conjunctive water use plays a great role in preventing groundwater depletion caused by the extensive pumping for various purposes. The groundwater contribution in optimal conjunctive water use is very high having a value of 92?% because of limited capacity of canal. The findings would be useful to the planners and decision makers for ensuring long-term water sustainability.

     

Application of Neural Networks and Optimization Model in Conjunctive Use of Surface Water and Groundwater

This study develops an optimization model for the large-scale conjunctive use of surface water and groundwater resources. The aim is to maximize public and irrigation water supplies subject to groundwater-level drawdown constraints. Linear programming is used to create the optimization model, which is formulated as a linear constrained objective function. An artificial neural network is trained by a flow modeling program at specific observation wells, and the network is then incorporated into the optimization model. The proposed methodology is applied to the Chou-Shui alluvial fan system, located in central Taiwan. People living in this region rely on large quantities of pumped water for their public and irrigation demands. This considerable dependency on groundwater has resulted in severe land subsidence in many coastal regions of the alluvial fan. Consequently, an efficient means of implementing large-scale conjunctive use of surface water and groundwater is needed to prevent further overuse of groundwater. Two different optimization scenarios are considered. The results given by the proposed model show that water-usage can be balanced with a stable groundwater level. Our findings may assist officials and researchers in establishing plans to alleviate land subsidence problems.     

Conjunctive water use for irrigation: Good theory,poor practice

There is growing interest in conjunctive water use in irrigation management. However, most systems are introducing ‘joint use’ of surface and groundwater to overcome problems of poor water delivery or quality, rather than systems which actually maximize water utility. This paper summarizes the findings of a literature review on conjunctive use research, including the variable objectives behind its promotion and the hydrological and agricultural planning models experimented with. Effective promotion and subsequent sound management of conjunctive use technologies in irrigation require interaction between a broad range of institutions, including rural development and agricultural support agencies as well as water management bodies.     

Optimal Reservoir Operation Based on Conjunctive Use of Surface Water and Groundwater Using Neuro-Fuzzy Systems

Reservoir operation cannot be carried out without due heed to surface water and groundwater resources, since neglecting either will have irreversible consequences. Optimal operation of the Zayandehrood Dam which supplies water into the Zayandehrood River basin in the central plateau of Iran is a case in point which warrants due consideration paid to both dam operation and the climate conditions in the region suffering from a history of successive droughts. The main objective of the present research is to develop operation rules for the Zayandehrood reservoir through a combined perspective of both surface and ground water resources using the fuzzy inference system, and adaptive neuro-fuzzy inference system. The objective is to determine the share of the Zayandehrood reservoir in meeting downstream water demands. For this purpose, the water shortage and the dramatic groundwater drawdown in the Zayandehrood River basin faced with in recent years have been studied in an attempt to develop operation models capable of controlling groundwater drawdown. The models indicate that not only can groundwater drawdown be controlled, but that it is also possible to establish a greater sustainability. Different operation models have been compared in terms of their operation criteria. Results show that the ANFIS model composed of optimal data enjoys a higher sustainability compared to others.     

Application of Genetic Algorithms and Artificial Neural Networks in Conjunctive Use of Surface and Groundwater Resources

Abstract

In this paper, a methodology for conjunctive use of surface and groundwater resources is developed using the combination of the Genetic Algorithms (GAs) and the Artificial Neural Networks (ANN). Water supply to agricultural demands, reduction of pumping costs and control of groundwater table fluctuations are considered in the objective function of the model. In the proposed model, the results of MODFLOW groundwater simulation model are used to train an ANN. The ANN as groundwater response functions is then linked to the GA based optimization model to develop the monthly conjunctive use operating policies. The model is applied to the surface and groundwater allocation for irrigation purposes in the southern part of Tehran. A new ANN is also trained and checked for developing the real-time conjunctive use operating rules.

The results show the significance of an integrated approach to surface and groundwater allocation in the study area. A simulation of the optimal policies shows that the cumulative groundwater table variation can be reduced to less than 4 meters from the current devastating condition. The results also show that the proposed model can effectively reduce the run time of the conjunctive use models through the composition of a GA-based optimization and a ANN-based simulation model.     

Applying Genetic Algorithm and Neural Network to the Conjunctive Use of Surface and Subsurface Water

The conjunctive use of surface and subsurface water is one of the most effective ways to increase water supply reliability with minimal cost and environmental impact. This study presents a novel stepwise optimization model for optimizing the conjunctive use of surface and subsurface water resource management. At each time step, the proposed model decomposes the nonlinear conjunctive use problem into a linear surface water allocation sub-problem and a nonlinear groundwater simulation sub-problem. Instead of using a nonlinear algorithm to solve the entire problem, this decomposition approach integrates a linear algorithm with greater computational efficiency. Specifically, this study proposes a hybrid approach consisting of Genetic Algorithm (GA), Artificial Neural Network (ANN), and Linear Programming (LP) to solve the decomposed two-level problem. The top level uses GA to determine the optimal pumping rates and link the lower level sub-problem, while LP determines the optimal surface water allocation, and ANN performs the groundwater simulation. Because the optimization computation requires many groundwater simulations, the ANN instead of traditional numerical simulation greatly reduces the computational burden. The high computing performance of both LP and ANN significantly increase the computational efficiency of entire model. This study examines four case studies to determine the supply efficiencies under different operation models. Unlike the high interaction between climate conditions and surface water resource, groundwater resources are more stable than the surface water resources for water supply. First, results indicate that adding an groundwater system whose supply productivity is just 8.67 % of the entire water requirement with a surface water supply first (SWSF) policy can significantly decrease the shortage index (SI) from 2.93 to 1.54. Second, the proposed model provides a more efficient conjunctive use policy than the SWSF policy, achieving further decrease from 1.54 to 1.13 or 0.79, depending on the groundwater rule curves. Finally, because of the usage of the hybrid framework, GA, LP, and ANN, the computational efficiency of proposed model is higher than other models with a purebred architecture or traditional groundwater numerical simulations. Therefore, the proposed model can be used to solve complicated large field problems. The proposed model is a valuable tool for conjunctive use operation planning.     

Conjunctive Operation of Reservoirs and Ponds Using a Simulation-Optimization Model of Irrigation Systems

Water resource management in arid agricultural irrigation regions is a great challenge for managers and decision makers. In some of those regions, many ponds have been built to ensure an adequate water supply for irrigation. Therefore, reservoirs and ponds should be managed conjunctively to minimize shortages of water. In this study, a new integrated mathematical model of conjunctive, or integrated, operation of reservoirs and ponds to maximize the water supply has been proposed for a reservoir-pond irrigation system. This objective has been achieved via the use of two models: an optimal model, which is used to determine the optimal discharge of reservoirs, and a simulation model, which considers the regulatory role of ponds and reservoirs and simulates their water supply to the irrigation system. An adaptive genetic algorithm has been employed in this study to solve the nonlinear and multi-dimensional reservoirs optimization problem. This methodology has been applied to the Yarkant River Basin to demonstrate its applicability, and three scenarios are presented. The main objective of the simulation-optimization model in the Yarkant River Basin is to minimize shortages in meeting irrigation demands for nine sub-irrigation systems subject to the constraint of ecological water transfer to the Tarim River. The optimizing effect of the model was particularly prominent under the third scenario, i.e., the XBD, MMK, and ART Reservoirs and 16 ponds conjunctively operated to meet the water demand of the YKB. The frequency of success (FS) in meeting agricultural water demands reaches up to 75%, and the value for ecological demand is 50.98%. The results demonstrate the importance of the conjunctive combined use approach for management of water resources in irrigation system of arid regions.     

Coupled Quantity-Quality Simulation-Optimization Model for Conjunctive Surface-Groundwater Use

Many water resources optimization problems involve conflicting objectives which the main goal is to find a set of optimal solutions on, or near to, Pareto front. In this study a multi-objective water allocation model was developed for optimization of conjunctive use of surface water and groundwater resources to achieve sustainable supply of agricultural water. Here, the water resource allocation model is based on simulation-optimization (SO) modeling approach. Two surrogate models, namely an Artificial Neural Network model for groundwater level simulation and a Genetic Programming model for TDS concentration prediction were coupled with NSGA-II. The objective functions involved: 1) minimizing water shortage relative to the water demand, 2) minimizing the drawdown of groundwater level, and 3) minimizing the groundwater quality changes. According to the MSE and R² criteria, the results showed that the surrogate models for prediction of groundwater level and TDS concentration performed favorably in comparison to the measured values at the number of observation wells. In Najaf Abad plain case study, the average drawdown was limited to 0.18 m and the average TDS concentration also decreased from 1257 mg/lit to 1229 mg/lit under optimal conditions.     

A Numerical Simulation Model for Conjunctive Water Use in Basin Irrigated Canal Command Areas

Basin irrigation is a common practice for growing water intensive crops like paddy. Irrigation water, when supplied through a network of canal, is often found to be inadequate to meet the crop water requirement uniformly throughout the irrigated command area. The most deprived are the cultivators of the lower end of the command, who resort to supplementing the crop water requirement by extractions from the ground. This practice is noticeable in irrigation system without a proper canal water distribution schedule and often result in water logging in the upper command regions contrasted with excessively depleted groundwater table in the lower commands. The present contribution attempts to model the conjunctive water use of such a canal irrigated command using physically based numerical sub-models for simulating surface flow, groundwater flow and the interlinking process of moisture movement through the unsaturated zone for a given quantum of supplied water and crop water demand. Individual models are validated to demonstrate their applicability in an integrated framework. Various plausible conjunctive water use scenarios are tested on a hypothetical command area practising basin irrigation to identify the best possible water distribution strategy under given constraints.     

A Demand Management Based Crop and Irrigation Planning Using the Simulation-Optimization Approach

Coupled simulation-optimization models are useful tools for solving optimum water allocation and crop planning problems. In this study, the optimum crops pattern in the Arayez plain in the Karkheh river basin in Iran is determined by integration of a network flow programming (NFP) based simulation model and the shuffle frog leaping optimization algorithm (SFLA) in the form of a simulation-optimization approach. MODSIM applies NFP for finding water allocations which by use of its customization ability, the benefit of water supply for the agricultural crops is calculated based on the agronomic equations. The objective function is to maximize the total net benefit gained from crops production where the decision variables which are the irrigation depths and the cultivation areas are optimized by SFLA. Results show that by use of the coupled SFLA-NFP model, the net benefit increases 12% comparing the present situation in the plain. Also, the sensitivity analyses on effective parameters indicate that the potential maximum yield and the net price of the crops yield in the market have a direct impact on the crops optimum cultivation area.     

Conjunctive Water Use Planning in an Irrigation Command Area

In the present study, an integrated soil water balance algorithm was coupled to a non-linear optimization model in order to carry out water allocation planning in complex deficit agricultural water resources systems based on an economic efficiency criterion. The LINGO 10.0, optimization package has been used to evolve at optimal allocation plan of surface and ground water for irrigation of multiple crops. The proposed model was applied for Qazvin Irrigation Command Area, a semi-arid region in Iran. Various scenarios of conjunctive use of surface and ground water along-with current and proposed cropping pattern have been explored. Some deficit irrigation practices were also investigated. The results indicate that conjunctive use practices are feasible and can be easily implemented in the study area, which would enhance the overall benefits from cropping activities. The study provides various possible operational scenarios of the branch canals of the command area in the common and dry condition, which can help managers in decision making for the optimum allocation plans of water resources within the different irrigation districts. The findings demonstrate that for deficit irrigation options, the mining allowance of ground water value of the command area is greatly reduced and ground water withdrawal may be also restricted to the recharge to maintain the river–aquifer equilibrium.     

Simulation-Optimization Modeling for Conjunctive Use Management under Hydrological Uncertainty

The canal water supply, which is the only source of irrigation, in the rice-dominated cropping system of the Hirakud canal command (eastern India) is able to meet only 54 % of the irrigation demand at 90 % probability of exceedance. Hence, considering groundwater as the supplemental source of irrigation, conjunctive use management study by combined simulation-optimization modelling was undertaken in order to predict the maximum permissible groundwater pumpage from the command area. Further, optimal land and water resources allocation model was developed to determine the optimal cropping pattern for maximizing net annual return. The modelling results suggested that 2.0 and 2.3 million m³ of groundwater can be pumped from the bottom aquifer during monsoon and non-monsoon seasons, respectively, at 90 % probability of exceedance of rainfall and canal water availability (PERC). Optimal cropping patterns and pumping strategies can lead to about 51.3–12.5 % increase in net annual return from the area at 10–90 % PERC. The sensitivity analysis of the model indicates that the variation in the market price of crops has very high influence on the optimal solution followed by the cost of cultivation and cultivable area. Finally, different future scenarios of land and water use were formulated for the command area. The adoption of optimal cropping patterns and optimal pumping strategies is strongly recommended for sustainable management of available land and water resources of the canal command under hydrological uncertainties.     

Conserving One Water Source at the Expense of Another: The Role of Surface Water Price in Adoption of Wells in a Conjunctive Use System

One potential side-effect of irrigation water rate reform is groundwater substitution. As surface water prices rise, irrigators may find it cheaper to rely on on-farm wells than a regional irrigation district. The impact of surface water price on well adoption is examined in a conjunctive use system where both surface water and groundwater are used to meet irrigator demand. Results indicate that as the price of surface water approaches 62% of the marginal cost of pumping groundwater, irrigators are more likely to have on-farm pumping capabilities. This result suggests that proposed water rate reforms by the United States Bureau of Reclamation may result in irrigators substituting groundwater for surface water by adopting on-farm wells.     

Multi-objective Planning for Conjunctive Use of Surface and Subsurface Water Using Genetic Algorithm and Dynamics Programming

The consideration of fixed cost and time-varying operating cost associated with the simultaneous conjunctive use of surface and subsurface water should be treated as a multi-objective problem due to the conflicting characteristics of these two objectives. In order to solve this multi-objective problem, a novel approach is developed herein by integrating the multi-objective genetic algorithm (MOGA), constrained differential dynamic programming (CDDP) and the groundwater simulation model ISOQUAD. A MOGA is used to generate the various fixed costs of reservoirs’ scale, generate a pattern of pumping/recharge, and estimate the non-inferior solutions set. A groundwater simulation model ISOQUAD is directly embedded to handle the complex dynamic relationship between the groundwater level and the generated pumping/recharge pattern. The CDDP optimization model is then adopted to distribute the optimal releases among reservoirs provided that reservoir capacities are known. Finally, the effectiveness of our proposed integrated model is verified by solving a water resources planning problem for the conjunctive use of surface and subsurface water in southern Taiwan.     

Technical and economic evaluation of the conjunctive use of surface and groundwater in the Campaspe Valley,north-central Victoria,Australia

This paper describes a technical and feasibility study of increasing groundwater usage to supplement surface water use in the Campaspe Valley in south-eastern Australia. An integrated model which simulates the surface and groundwater processes, as well as the interactions between the processes, is used to determine the sustainable long-term groundwater pumping yields. The model also provides estimates of groundwater fluxes for various management options of increasing groundwater usage. These estimates are used to assist an economic analysis to determine the relative merits of various options for the conjunctive use of surface and groundwater resources. The pumping costs, value of water and tangible salinity benefits from lowering high water-tables and reducing salt load are considered in the economic analysis. The methodology is also relevant for other studies looking into the conjunctive use of surface and groundwater resources throughout the Murray Basin and elsewhere.     

Appraisal and optimization of agricultural water use in large irrigation schemes: II. Applications

In this paper, the theoretical approach presented in Part I is demonstrated by means of case studies on the irrigation schemes of Rio Mendoza and Rio Tunuyán in the Province of Mendoza, Argentina. The object of the case studies was the determination of optimal allocation of surface water to reduce the use of groundwater. Current and optimal conjunctive allocation of ground and surface water is studied by means of the developed simulation and optimization models.The second case study was designed on the basis of the experience gathered during the first one: each step of the case studies is compared. The comparison between the two case studies illustrates how to apply the proposed approach when the amount and quality of available data are different. For the more detailed Rio Tunuyán study, we determined the following physical characteristics of all terminal nodes: on-farm rotational intervals, mean water application depth, actual soil water storage capacity, crop water requirements, depth of groundwater table, aquifer transmissivity, and efficiency of groundwater use.The performance of the entire system is sensitive to changes in the water application depth, as it was shown by a simulation study.The application of our optimization approach to the conjunctive use of ground- and surface water showed that the total water requirements over a year can be met by a reassignment of water that reduces the total costs of a great amount.     

Simulation-Optimization Modelling for Sustainable Groundwater Management in a Coastal Basin of Orissa, India

The Balasore coastal groundwater basin of Orissa in eastern India is under a serious threat of overdraft and seawater intrusion. Two optimization models were developed in this study for the efficient utilization of water resources in Balasore basin during non-monsoon periods: (a) a non-linear hydraulic management model for optimal pumpage, and (b) a linear optimization model for optimal cropping pattern in integration with a calibrated and validated groundwater flow simulation model. Based on the simulation-optimization modeling results, optimal pumping schedules, cropping patterns, and corresponding groundwater conditions are presented for three scenarios viz., wet, normal and dry years. It was found that optimal pumping schedules and corresponding cropping patterns differed significantly under the three scenarios, and the groundwater levels improved significantly under the optimal hydraulic conditions compared to the existing condition. In dry years, the groundwater levels under the present pumping pattern and the optimal pumpage indicated that the non-monsoon pumpage should not exceed the optimal pumpage in the absence of remedial measures in the basin. It is concluded that in order to ensure sustainable groundwater utilization in the basin, the optimal cropping pattern and pumping schedule should be adopted by the farmers.