Advantages of the analytic element method for the solution of groundwater management problems

In the simulation‐optimization approach, a coupled optimization and groundwater flow/transport model is used to solve groundwater management problems. The efficiency of the numerical method, which is used to simulate the groundwater flow, is one the major reason to obtain the best solution for a management problem. This study was carried out to examine the advantages of the analytic element method (AEM) in the simulation‐optimization approach, for the solution of groundwater management problems. For this study, the AEM and finite difference method (FDM) based flow models were developed and coupled with the particle swarm optimization (PSO)‐based optimization model. Furthermore, the AEM‐PSO and FDM‐PSO models developed were applied in hypothetical as well as real field conditions to address groundwater management problems and the results were compared. For the real field situation, the models developed were applied to the Dore River basin in France to minimize the installation and operational cost of new pumping wells taking the location and discharge of the pumping wells as decision variables. The constraints of the problem were identified with the help of stakeholders and water authority officials. The AEM flow model was developed to facilitate the management model particularly when at each iteration, the optimization model calls for a simulation model to calculate the values of groundwater heads. The results show that, at some points, the AEM‐PSO model is efficient in identifying the optimal location of wells and consequently results in optimal costs, sometimes difficult when using the FDM. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimal control algorithm and neural network for dynamic groundwater management

Researchers have found that obtaining optimal solutions for groundwater resource‐planning problems, while simultaneously considering time‐varying pumping rates, is a challenging task. This study integrates an artificial neural network (ANN) and constrained differential dynamic programming (CDDP) as simulation‐optimization model, called ANN‐CDDP. Optimal solutions for a groundwater resource‐planning problem are determined while simultaneously considering time‐varying pumping rates. A trained ANN is used as the transition function to predict ground water table under variable pumping conditions. The results show that the ANN‐CDDP reduces computational time by as much as 94·5% when compared to the time required by the conventional model. The proposed optimization model saves a considerable amount of computational time for solving large‐scale problems. Copyright © 2009 John Wiley & Sons, Ltd.     

Model-aided design and optimization of artificial recharge-pumping systems

Abstract

This paper presents a methodology for the design and optimization of artificial recharge-pumping systems (ARPS). The objective of ARPS is to provide a maximum abstraction rate through artificial recharge, while meeting two operational constraints: (a) the influences of the system operation on groundwater levels should be no more than 25 mm in the vicinity of the system; and (b) the travel time of the infiltrated water from the recharge pond to the pumping wells should be more than 60 days. The combined use of a 3-dimensional generic groundwater simulation model with particle tracking analyses has identified the two best ARPS systems: the circular pond system and the island system. By coupling the simulation model with linear and mixed integer programming optimization, the optimal pumping scheme (number, locations and rates of the pumping wells) has been determined. An unsteady state model has been used to simulate the response of the operation of the two systems under natural seasonal variations. The implementation aspects of the two systems are compared.     

Parameter Estimation for Groundwater Models under Uncertain Irrigation Data

The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression‐based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least‐squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least‐squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least‐squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p < 0.05) bias in estimated parameters and model predictions that persist despite calibrating the models to different calibration data and sample sizes. However, by directly accounting for the irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.     

A stochastic optimization model based on adaptive feedback correction process and surrogate model uncertainty for DNAPL-contaminated groundwater remediation design

A stochastic optimization model based on an adaptive feedback correction process and surrogate model uncertainty was proposed and applied for remediation strategy design at a dense non-aqueous phase liquids (DNAPL)-contaminated groundwater site. One hundred initial training samples were obtained using the Latin hypercube sampling method. A surrogate model of a multiphase flow simulation model was constructed based on these samples employing the self-adaptive particle swarm optimization kriging (SAPSOKRG) method. An optimization model was built, using the SAPSOKRG surrogate model as a constraint. Then, an adaptive feedback correction process was designed and applied to iteratively update the training samples, surrogate model, and optimization model. Results showed that the training samples, the surrogate model, and the optimization model were effectively ameliorated. However, the surrogate model is an approximation of the simulation model, and some degree of uncertainty exists even though the surrogate model was ameliorated. Therefore, residuals between the surrogate model and the simulation model were calculated, and an uncertainty analysis was conducted. Based on the uncertainty analysis results, a stochastic optimization model was constructed and solved to obtain optimal remediation strategies at different confidence levels (60, 70, 80, 90, 95%) and under different remediation objectives (average DNAPL removal rate ≥?70,?≥?75,?≥?80,?≥?85,?≥?90%). The optimization results demonstrated that the higher the confidence level and remediation objective, the more expensive was remediation. Therefore, decision makers can weigh remediation costs, confidence levels, and remediation objectives to make an informed choice. This also allows decision makers to determine the reliability of a selected strategy and provides a new tool for DNAPL-contaminated groundwater remediation design.     

Bayesian model averaging assessment on groundwater management under model structure uncertainty

This study introduces Bayesian model averaging (BMA) to deal with model structure uncertainty in groundwater management decisions. A robust optimized policy should take into account model parameter uncertainty as well as uncertainty in imprecise model structure. Due to a limited amount of groundwater head data and hydraulic conductivity data, multiple simulation models are developed based on different head boundary condition values and semivariogram models of hydraulic conductivity. Instead of selecting the best simulation model, a variance-window-based BMA method is introduced to the management model to utilize all simulation models to predict chloride concentration. Given different semivariogram models, the spatially correlated hydraulic conductivity distributions are estimated by the generalized parameterization (GP) method that combines the Voronoi zones and the ordinary kriging (OK) estimates. The model weights of BMA are estimated by the Bayesian information criterion (BIC) and the variance window in the maximum likelihood estimation. The simulation models are then weighted to predict chloride concentrations within the constraints of the management model. The methodology is implemented to manage saltwater intrusion in the “1,500-foot” sand aquifer in the Baton Rouge area, Louisiana. The management model aims to obtain optimal joint operations of the hydraulic barrier system and the saltwater extraction system to mitigate saltwater intrusion. A genetic algorithm (GA) is used to obtain the optimal injection and extraction policies. Using the BMA predictions, higher injection rates and pumping rates are needed to cover more constraint violations, which do not occur if a single best model is used.     

Optimal Decision Making Algorithm for Managed Aquifer Recharge and Recovery Operation Using Near Real‐Time Data: Benchtop Scale Laboratory Demonstration

Aquifers show troubling signs of irreversible depletion as climate change, population growth, and urbanization lead to reduced natural recharge rates and overuse. One strategy to sustain the groundwater supply is to recharge aquifers artificially with reclaimed water or stormwater via managed aquifer recharge and recovery (MAR) systems. Unfortunately, MAR systems remain wrought with operational challenges related to the quality and quantity of recharged and recovered water stemming from a lack of data‐driven, real‐time control. This paper presents a laboratory scale proof‐of‐concept study that demonstrates the capability of a real‐time, simulation‐based control optimization algorithm to ease the operational challenges of MAR systems. Central to the algorithm is a model that simulates water flow and transport of dissolved chemical constituents in the aquifer. The algorithm compensates for model parameter uncertainty by continually collecting data from a network of sensors embedded within the aquifer. At regular intervals the sensor data is fed into an inversion algorithm, which calibrates the uncertain parameters and generates the initial conditions required to model the system behavior. The calibrated model is then incorporated into a genetic algorithm that executes simulations and determines the best management action, for example, the optimal pumping policy for current aquifer management goals. Experiments to calibrate and validate the simulation‐optimization algorithm were conducted in a small two‐dimensional synthetic aquifer under both homogeneous and heterogeneous packing configurations. Results from initial experiments validated the feasibility of the approach and suggested that our system could improve the operation of full‐scale MAR facilities.     

A method of accelerating transport simulation when groundwater pumping is simulated

In solving groundwater transport problems with numerical models, the computation time (CPU processing time) of transport simulation is approximately inversely proportional to the transport time-step size. Therefore, large time-step sizes are favorable for achieving short computation time. However, transport time-step size must be sufficiently small to avoid numerical instability if an explicit scheme is used (and to guarantee enough model accuracy if an implicit scheme is used). For a transport model involving groundwater pumping, a small transport time-step size is often required due to the high groundwater velocities near the pumping well. Small grid spacing often specified near the pumping well also limits the time-step size. This paper presents a method to increase transport time-step size in a transport model when groundwater pumping is simulated. The key to this approach is to numerically decrease the groundwater seepage velocities in grid cells near the pumping well by increasing the effective porosity so that the transport time-step size can be increased without violating stability constraints. Numerical tests reveal that by using the proposed method, the computation time of transport simulation can be reduced significantly, while the transport simulation results change very little.     

Optimal pumping locations of skimming wells

Abstract

A real-life problem involving pumping of groundwater from a series of existing wells along a river flood plain underlain with geologically saline water is examined within a conceptual framework. Unplanned pumping results in upconing of saline water. Therefore, it is necessary to determine optimal locations of fixed capacity pumping wells in space and time from a set of pre-selected candidate wells that minimize total salinity concentration in space and time. The nonlinear, non-convex, combinatorial problem involving zero—one decision variables is solved in a simulation—optimization (S/O) framework. Optimization is accomplished by using simulated annealing (SA)—a search algorithm. The computational burden is primarily managed by replacing the numerical model with a surrogate simulator—artificial neural network (ANN). The computational burden is further reduced through intuitive algorithmic guidance. The model results suggest that the skimming wells must be operated from optimal locations such that they are staggered in space and time to obtain least saline water.     

Optimal design of pumping tests in leaky aquifers for stream depletion analysis

We analyze the optimal design of a pumping test for estimating hydrogeologic parameters that are subsequently used to predict stream depletion caused by groundwater pumping in a leaky aquifer. A global optimization method is used to identify the test’s optimal duration and the number and locations of observation wells. The objective is to minimize predictive uncertainty (variance) of the estimated stream depletion, which depends on the sensitivities of depletion and drawdown to relevant hydrogeologic parameters. The sensitivities are computed analytically from the solutions of Zlotnik and Tartakovsky [Zlotnik, V.A., Tartakovsky, D.M., 2008. Stream depletion by groundwater pumping in leaky aquifers. ASCE Journal of Hydrologic Engineering 13, 43–50] and the results are presented in a dimensionless form, facilitating their use for planning of pumping test at a variety of sites with similar hydrogeological settings. We show that stream depletion is generally very sensitive to aquitard’s leakage coefficient and stream-bed’s conductance. The optimal number of observation wells is two, their optimal locations are one close to the stream and the other close to the pumping well. We also provide guidelines on the test’s optimal duration and demonstrate that under certain conditions estimation of aquitard’s leakage coefficient and stream-bed’s conductance requires unrealistic test duration and/or signal-to-noise ratio.     

Application of Harmony Search algorithm to the solution of groundwater management models

This study proposes a groundwater resources management model in which the solution is performed through a combined simulation-optimization model. A modular three-dimensional finite difference groundwater flow model, MODFLOW is used as the simulation model. This model is then combined with a Harmony Search (HS) optimization algorithm which is based on the musical process of searching for a perfect state of harmony. The performance of the proposed HS based management model is tested on three separate groundwater management problems: (i) maximization of total pumping from an aquifer (steady-state); (ii) minimization of the total pumping cost to satisfy the given demand (steady-state); and (iii) minimization of the pumping cost to satisfy the given demand for multiple management periods (transient). The sensitivity of HS algorithm is evaluated by performing a sensitivity analysis which aims to determine the impact of related solution parameters on convergence behavior. The results show that HS yields nearly same or better solutions than the previous solution methods and may be used to solve management problems in groundwater modeling.     

Modeling Stream-Aquifer Interactions Under Seasonal Groundwater Pumping and Managed Aquifer Recharge

In South Korea, a significant amount of groundwater is used for the heating of water-curtain insulated greenhouses during the winter dry season, which had led to problems of groundwater depletion. A managed aquifer recharge (MAR) project is currently underway with the goal of preventing such groundwater depletion in a typical cultivation area, located on an alluvial aquifer near the Nam River. In the present study, FEFLOW, a three-dimensional finite element model, was used to evaluate different strategies for MAR of the cultivation areas. A conceptual model was developed to simulate the stream-aquifer dynamics under the influence of seasonal groundwater pumping and MAR. The optimal rates and duration of MAR were assessed by analyzing the recovery of the groundwater levels and the change in the groundwater temperature. The simulation results indicate that a MAR rate of 8000 m³/d effectively restores the groundwater level when the injection wells are located inside the groundwater depletion area. It is also demonstrated that starting the MAR before the beginning of the seasonal pumping is more effective. Riverbank filtration is preferable for securing the injection water owing to plentiful source of induced recharge from the river. Locating the pumping wells adjacent to the river where there are thick permeable layers could be a good strategy for minimizing decreases in the groundwater level and temperature.     

Optimizing separate phase light hydrocarbon recovery from contaminated unconfined aquifers

A modeling approach is presented that optimizes separate phase recovery of light non-aqueous phase liquids (LNAPL) for a single dual-extraction well in a homogeneous, isotropic unconfined aquifer. A simulation/regression/optimization (S/R/O) model is developed to predict, analyze, and optimize the oil recovery process. The approach combines detailed simulation, nonlinear regression, and optimization. The S/R/O model utilizes nonlinear regression equations describing system response to time-varying water pumping and oil skimming. Regression equations are developed for residual oil volume and free oil volume. The S/R/O model determines optimized time-varying (stepwise) pumping rates which minimize residual oil volume and maximize free oil recovery while causing free oil volume to decrease a specified amount. This S/R/O modeling approach implicitly immobilizes the free product plume by reversing the water table gradient while achieving containment. Application to a simple representative problem illustrates the S/R/O model utility for problem analysis and remediation design. When compared with the best steady pumping strategies, the optimal stepwise pumping strategy improves free oil recovery by 11.5% and reduces the amount of residual oil left in the system due to pumping by 15%. The S/R/O model approach offers promise for enhancing the design of free phase LNAPL recovery systems and to help in making cost-effective operation and management decisions for hydrogeologists, engineers, and regulators.     

Grey fuzzy optimization model for water quality management of a river system

A grey fuzzy optimization model is developed for water quality management of river system to address uncertainty involved in fixing the membership functions for different goals of Pollution Control Agency (PCA) and dischargers. The present model, Grey Fuzzy Waste Load Allocation Model (GFWLAM), has the capability to incorporate the conflicting goals of PCA and dischargers in a deterministic framework. The imprecision associated with specifying the water quality criteria and fractional removal levels are modeled in a fuzzy mathematical framework. To address the imprecision in fixing the lower and upper bounds of membership functions, the membership functions themselves are treated as fuzzy in the model and the membership parameters are expressed as interval grey numbers, a closed and bounded interval with known lower and upper bounds but unknown distribution information. The model provides flexibility for PCA and dischargers to specify their aspirations independently, as the membership parameters for different membership functions, specified for different imprecise goals are interval grey numbers in place of a deterministic real number. In the final solution optimal fractional removal levels of the pollutants are obtained in the form of interval grey numbers. This enhances the flexibility and applicability in decision-making, as the decision-maker gets a range of optimal solutions for fixing the final decision scheme considering technical and economic feasibility of the pollutant treatment levels. Application of the GFWLAM is illustrated with case study of the Tunga–Bhadra river system in India.     

Optimal Design of Multitype Groundwater Monitoring Networks Using Easily Accessible Tools

Monitoring networks are expensive to establish and to maintain. In this paper, we extend an existing data‐worth estimation method from the suite of PEST utilities with a global optimization method for optimal sensor placement (called optimal design) in groundwater monitoring networks. Design optimization can include multiple simultaneous sensor locations and multiple sensor types. Both location and sensor type are treated simultaneously as decision variables. Our method combines linear uncertainty quantification and a modified genetic algorithm for discrete multilocation, multitype search. The efficiency of the global optimization is enhanced by an archive of past samples and parallel computing. We demonstrate our methodology for a groundwater monitoring network at the Steinlach experimental site, south‐western Germany, which has been established to monitor river—groundwater exchange processes. The target of optimization is the best possible exploration for minimum variance in predicting the mean travel time of the hyporheic exchange. Our results demonstrate that the information gain of monitoring network designs can be explored efficiently and with easily accessible tools prior to taking new field measurements or installing additional measurement points. The proposed methods proved to be efficient and can be applied for model‐based optimal design of any type of monitoring network in approximately linear systems. Our key contributions are (1) the use of easy‐to‐implement tools for an otherwise complex task and (2) yet to consider data‐worth interdependencies in simultaneous optimization of multiple sensor locations and sensor types.     

Multiobjective fuzzy optimization for sustainable groundwater management using particle swarm optimization and analytic element method

Groundwater management involves conflicting objectives as maximization of discharge contradicts the criteria of minimum pumping cost and minimum piping cost. In addition, available data contains uncertainties such as market fluctuations, variations in water levels of wells and variations of ground water policies. A fuzzy model is to be evolved to tackle the uncertainties, and a multiobjective optimization is to be conducted to simultaneously satisfy the contradicting objectives. Towards this end, a multiobjective fuzzy optimization model is evolved. To get at the upper and lower bounds of the individual objectives, particle Swarm optimization (PSO) is adopted. The analytic element method (AEM) is employed to obtain the operating potentio metric head. In this study, a multiobjective fuzzy optimization model considering three conflicting objectives is developed using PSO and AEM methods for obtaining a sustainable groundwater management policy. The developed model is applied to a case study, and it is demonstrated that the compromise solution satisfies all the objectives with adequate levels of satisfaction. Sensitivity analysis is carried out by varying the parameters, and it is shown that the effect of any such variation is quite significant. Copyright © 2015 John Wiley & Sons, Ltd.     

A physically constrained wavelet-aided statistical model for multi-decadal groundwater dynamics predictions

A physically constrained wavelet-aided statistical model (PCWASM) is presented to analyse and predict monthly groundwater dynamics on multi-decadal or longer time scales. The approach retains the simplicity of regression modelling but is constrained by temporal scales of processes responsible for groundwater level variation, including aquifer recharge and pumping. The methodology integrates statistical correlations enhanced with wavelet analysis into established principles of groundwater hydraulics including convolution, superposition and the Cooper–Jacob solution. The systematic approach includes (1) identification of hydrologic trends and correlations using cross-correlation and multi-time scale wavelet analyses; (2) integrating temperature-based evapotranspiration and groundwater pumping stresses and (3) assessing model prediction performances using fixed-block k-fold cross-validation and split calibration-validation methods. The approach is applied at three hydrogeologicaly distinct sites in North Florida in the United States using over 40 years of monthly groundwater levels. The systematic approach identifies two patterns of cross-correlations between groundwater levels and historical rainfall, indicating low-frequency variabilities are critical for long-term predictions. The models performed well for predicting monthly groundwater levels from 7 to 22 years with less than 2.1 ft (0.7 m) errors. Further evaluation by the moving-block bootstrap regression indicates the PCWASM can be a reliable tool for long-term groundwater level predictions. This study provides a parsimonious approach to predict multi-decadal groundwater dynamics with the ability to discern impacts of pumping and climate change on aquifer levels. The PCWASM is computationally efficient and can be implemented using publicly available datasets. Thus, it should provide a versatile tool for managers and researchers for predicting multi-decadal monthly groundwater levels under changing climatic and pumping impacts over a long time period.     

Risk minimization in water quality control problems of a river system

Methodologies are presented for minimization of risk in a river water quality management problem. A risk minimization model is developed to minimize the risk of low water quality along a river in the face of conflict among various stake holders. The model consists of three parts: a water quality simulation model, a risk evaluation model with uncertainty analysis and an optimization model. Sensitivity analysis, First Order Reliability Analysis (FORA) and Monte–Carlo simulations are performed to evaluate the fuzzy risk of low water quality. Fuzzy multiobjective programming is used to formulate the multiobjective model. Probabilistic Global Search Laussane (PGSL), a global search algorithm developed recently, is used for solving the resulting non-linear optimization problem. The algorithm is based on the assumption that better sets of points are more likely to be found in the neighborhood of good sets of points, therefore intensifying the search in the regions that contain good solutions. Another model is developed for risk minimization, which deals with only the moments of the generated probability density functions of the water quality indicators. Suitable skewness values of water quality indicators, which lead to low fuzzy risk are identified. Results of the models are compared with the results of a deterministic fuzzy waste load allocation model (FWLAM), when methodologies are applied to the case study of Tunga–Bhadra river system in southern India, with a steady state BOD–DO model. The fractional removal levels resulting from the risk minimization model are slightly higher, but result in a significant reduction in risk of low water quality.     

Multivariate approaches optimize locations of groundwater pumping facilities for different hydrogeological scales

A number of optimization approaches regarding the design location of groundwater pumping facilities in heterogeneous porous media have elicited little discussion. However, the location of groundwater pumping facilities is an important factor because it affects water resource usage. This study applies two optimization approaches to estimate the best recharge zone and suitable locations of the pumping facilities in southwestern Taiwan for different hydrogeological scales. First, for the regional scale, this study employs numerical modelling, MODFLOW‐96, to simulate groundwater direction and the optimal recharge zone in the study area. Based on the model's calibration and verification results, this study preliminarily utilizes the simulated spatial direction of groundwater and compares the safe yield for each well group in order to determine the best recharge zone. Additionally, for the local scale, the micro‐hydrogeological characteristics are considered before determining the design locations of the pumping facilities. According to drawdown record data from six observation wells derived from pumping tests at the best recharge area, this study further utilizes the modified artificial neural network approach to improve the accuracy of the estimation parameters as well as to analyse the direction and anisotropy of the hydraulic conductivities of an equivalent homogeneous aquifer. The results suggested that the best locations for the pumping facilities are along the more permeable major direction. Copyright © 2011 John Wiley & Sons, Ltd.