Groundwater Monitoring Network Design Research Articles

A novel groundwater monitoring network design framework for long-term and economical data monitoring

The groundwater monitoring network (GMN) design balancing prediction accuracy with cost minimization has paramount importance in depth to groundwater (DTW) monitoring, modeling, and management. This study demonstrates a robust and transferable GMN design framework for the underexplored statistically homogenous DTW to facilitate appropriate, long-term, cost-effective, and regional-scale groundwater monitoring. The inherent homogeneity in regional scale DTW data was investigated to cluster the observation wells into 18 strongly structured clusters/strata using optimal cluster number (k = 18), corresponding to the highest mean silhouette score of 0.72 and the K-means clustering algorithm. The clustered observation wells were used to evaluate the cluster random sampling (CRS) and stratified random sampling (SRS) for GMN design. The sampling techniques were evaluated to capture the spatial variability of DTW over the whole study region through spatial modeling at an adequate accuracy by the inverse distance weighting interpolation tools. The performance metrics for SRS recommended it as a more robust and appropriate technique for regional-scale GMN design than CRS. Then, the GMN was designed with 540 wells corresponding to 70% sampling under SRS for accurate, cost-effective, and long-term groundwater monitoring. By reducing 30% of observation wells, the cost-saving can contribute to cutting-edge monitoring infrastructure development. Additionally, the need-based balancing of the accuracy in spatial modeling and monitoring cost may further moderate the number of observation wells using the performance metrics of SRS corresponding to sampling percentages. Conclusively, this novel and transferable framework can be adopted globally with internally homogenous DTW data for effective GMN design.

Design of groundwater monitoring network for coastal aquifers based on seawater intrusion vulnerability: A case study of Mandaue City, Philippines

Groundwater is an essential source of freshwater. However, this natural resource exhibits high vulnerability to contamination due to human activities and natural phenomena. Over extraction of groundwater and sea level rise have hastened seawater intrusion in aquifers that lie along the coasts. This has led to the accelerated deterioration of groundwater quality in coastal aquifers. Thus, there is an urgent need to develop tools to monitor groundwater resources. In this study, a framework of selecting priority areas for monitoring was applied to Mandaue City, Philippines. The monitoring areas were selected based on their vulnerability to seawater intrusion using the GALDIT-index. A collection of 77 initial public sites were investigated as potential locations of monitoring wells. The resulting network determined 45 priority areas located across the study area wherein monitoring wells should be located for the long-term observation of groundwater quality. These sites are composed of 28 from the initially investigated locations and 17 proposed sites which were determined to satisfy the required number of wells lacking in certain areas. The results of this study are intended to aid decision makers in creating groundwater management plans.

Selecting wells for an optimal design of groundwater monitoring network based on monitoring priority map: A Kish Island case study

This paper presents a novel approach, i.e. a combination of gamma test and monitoring priority map, for optimal design of groundwater monitoring network (GMN) by considering the cumulative effects of industries, human activities, and natural factors on the groundwater quality. The proposed method was successfully applied to design an optimal network for groundwater salinity monitoring on Kish Island, Persian Gulf. The priority map of groundwater salinity monitoring was obtained based on the GALDIT index and two new factors including the average fluctuation of groundwater electrical conductivity (F) and distance from industries discharging saline effluents (P). The optimal number of monitoring wells was determined using a data analysis based on gamma test method. Then, a practical algorithm was presented to determine the optimal location of monitoring wells. Based on the results, the optimal number of monitoring wells is 110 and their location have an equitable distribution on the whole island.

Groundwater Monitoring Network Design Using Optimized DRASTIC Method and Capture Zone Analysis

Groundwater Monitoring Network Design Using Optimized DRASTIC Method and Capture Zone Analysis

Application of MCLP and LINGO methods to optimal design of groundwater monitoring network in an oil refinery site

Abstract Groundwater-monitoring network is a set of boreholes (wells) that is used to monitor the water table fluctuation and to detect groundwater contamination. In this research, the maximal covering location problem (MCLP) method is employed to discretize the area, and the LINGO modeling program is used to optimize the number of boreholes. Tabriz oil refinery at the northwest of Iran with high pollution potential that imposes a serious threat to the beneath multilayered aquifer was chosen to evaluate the feasibility of these techniques in field scale. The location and content of storage tanks, leakage history, groundwater flow direction, contaminated well location and the facilities leakage potential are considered as the weighting factors to calculate the number and location of the optimal wells. As a result of optimization, the initial estimated number of boreholes by the MCLP model for the study area is reduced from 349 to 184. A high density of optimal boreholes is allocated to refining zone and oil storing yard, especially near tanks containing dangerous substances due to their toxicity and potential for contaminating water. A vulnerability zoning map prepared using the analytical hierarchy process method indicates a suitable conformation between locations of the boreholes and the vulnerable areas.

Optimal Design of Groundwater Monitoring Network Using the Combined Election-Kriging Method

Groundwater monitoring requires a great deal of cost and time that optimizing the quantitative groundwater monitoring network with selecting the optimal number of sampling wells and determining their optimal location for reducing the cost and time of quantitative groundwater assessment are necessary. The data from 110 observation wells of Neyshabur plain in Iran range from the year 1986 to 2016 were studied. The combined Election-Kriging method was used to analyze these data, and the Pareto chart was plotted to determine the optimal number and location in two scenarios. The first scenario was to determine the optimal wells location among the existing wells and the second scenario was to determine the optimal wells location for monitoring groundwater levels throughout the plain. To limit the search space, the maximum and minimum number of monitoring network wells were selected 30 and 85 respectively. Based on the results, the selected method accurately provided the appropriate location of wells, so that in the first scenario, the RMSE values ​​for the number of wells were in the range of 0.71 to 2.34 m. which are acceptable. In the second scenario, the RMSE values ​​of between 1.04 and 2.89 m were obtained, which are appropriate values ​​according to the objective of the problem. Also, the distribution of the wells selected in the area is also uniform in most numbers according to the second scenario, which indicates the good accuracy of the method.

Optimal design of groundwater monitoring networks using gamma test theory

Gamma test theory (GTT) is introduced as a novel method to determine the optimal number and location of groundwater monitoring wells without requiring temporal monitoring data. This method is based on the calculation of a statistic, called gamma, for the data of one monitoring period. The optimal wells are selected such that while they have the lowest gamma value, a further increase in the number of wells does not cause much change in their gamma value. The method was applied to the design of an optimal monitoring network for groundwater electrical conductivity (EC) on Kish Island, Hormozgan Province, Iran. The water EC of 55 wells, selected from 244 existing wells, was measured during one monitoring period on Kish Island and their latitude and longitude were recorded. A groundwater EC monitoring network was optimized using a GTT-based optimization algorithm. Based on the results, to estimate the spatial distribution of groundwater EC on Kish Island with maximum achievable accuracy, it was necessary to monitor at least 110 wells, which were identified. Finally, the water EC of the proposed wells was monitored in three monitoring periods and the proposed network was evaluated in these periods. Results indicated that the proposed wells are also optimum in these periods and the spatial distribution of groundwater EC can be estimated with maximum achievable accuracy using the EC data of the proposed 110 wells. The current study provides a time- and cost-effective method to achieve an efficient groundwater monitoring network especially when there is data limitation.

Optimal Design and Prediction-Independent Verification of Groundwater Monitoring Network

In this study, we developed a workflow that applies a complex groundwater model for purpose-driven groundwater monitoring network design and uses linear uncertainty analysis to explore the predictive dependencies and provide insights into the veracity of the monitoring design. A numerical groundwater flow model was used in a probabilistic modelling framework for obtaining the spatial distribution of predicted drawdown for a wide range of plausible combination of uncertain parameters pertaining to the deep sedimentary basin and groundwater flow processes. Reduced rank spatial prediction was used to characterize dominant trends in these spatial drawdown patterns using empirical orthogonal functions (EOF). A differential evolution algorithm was used to identify optimal locations for multi-level piezometers for collecting groundwater pressure data to minimize predictive uncertainty in groundwater drawdown. Data-worth analysis helps to explore the veracity of the design by using only the sensitivities of the observations to predictions independent of the absolute values of predictions. A 10-bore monitoring network that collects drawdown data from multiple depths at each location was designed. The data-worth analysis revealed that the design honours sensitivities of the predictions of interest to parameters. The designed network provided relatively high data-worth for minimizing uncertainty in the drawdown prediction at locations of interest.

Surrogate assisted multi-objective robust optimization for groundwater monitoring network design

The robust optimization of groundwater quality monitoring network is subject to many conflicting objectives and high level of uncertainty in hydraulic conductivity. This study develops a two-stage stochastic optimization framework including the uncertainty quantification using a cheap-to-evaluate surrogate model and an improved epsilon multi-objective noisy memetic algorithm (ε-MONMA) for monitoring network design. The surrogate model based on sparse polynomial chaos expansion (PCE) is constructed to replace expensive simulation model in the uncertainty quantification of concentrations at the pre-defined monitoring locations for reducing huge computational cost. Additionally, the scenario discovery strategy using sparse PCE model is applied to filter a typical scenario set and the centroid of contaminant plume is used as the diversity metric, which avoids enumerating all possible contamination plumes caused by the uncertain K-field in the optimization. The proposed algorithm is then employed to solve stochastic management model to achieve robust monitoring design, indicating the insensitivity of monitoring design to plume uncertainty no matter which of the many possible scenarios becomes the true distribution of contamination under the true K-field. A synthetic aquifer considering uncertainty in hydraulic conductivity is designed to optimize monitoring network design. The Pareto-optimal solutions to the synthetic example are achieved under three of plume scenario sets defined at deterministic scenario (Scenario A0), Monte Carlo based scenario discovery (Scenario A1) and surrogate assisted scenario discovery (Scenario A2), respectively. Comprehensive analysis demonstrates that the monitoring design based on Scenario A2 outperforms either of the two designs based on Scenarios A0 and A1 in terms of the improvement of robustness of designs evaluated against the typical scenario set. Meanwhile, the performance of monitoring network deteriorates as the uncertainty of plume (noisy strength) increases, indicating the significance of reducing parameter uncertainty in groundwater monitoring design. The research findings show that the developed stochastic optimization framework is a computationally efficient and promising tool for multi-objective design of groundwater monitoring network under uncertainty.

Search Space Representation and Reduction Methods to Enhance Multiobjective Water Supply Monitoring Design

AbstractOptimal design of groundwater monitoring networks is challenging due to (1) conflicting objectives for assessing the performance of candidate monitoring networks, (2) uncertainty in system dynamics and hydrogeological context, and (3) the large decision space of possible monitoring‐well positions (also termed the search space). The immensity of the search space poses a significant challenge for modern multiobjective optimization tools. This study introduces two approaches that improve the efficiency and effectiveness of evolutionary multiobjective optimization tools when solving monitoring design problems. We show how a careful mathematical representation of the monitoring design search space and reductions of possible monitoring‐well positions enhance the solution and attainment of decision‐relevant multiobjective trade‐offs in monitoring quality. We demonstrate the value of our improved representation and reduction techniques on a three‐objective monitoring network design problem focused on urban source water protection (termed the U_Protect benchmarking problem). U_Protect abstracts a real‐world case study within an urban drinking‐water well catchment, including inaccessible and restricted areas for monitoring‐well installation, and random heterogeneities in the conductivity field. Our representation and reduction methods significantly enhance the effectiveness, efficiency, and reliability of the optimization. Our proposed framework shifts focus to the most impactful monitoring design decisions while also enhancing decision makers understanding of key performance trade‐offs. In combination, our proposed representation and reduction techniques have significant promise for enhancing the size and the scope of combinatorial monitoring problems that can be explored.

Optimal design of groundwater-level monitoring networks

Abstract Groundwater monitoring plays a significant role in groundwater management. This study presents an optimization method for designing groundwater-level monitoring networks. The proposed design method was used in the Eshtehard aquifer, in central Iran. Three scenarios were considered to optimize the locations of the observation wells: (1) designing new monitoring networks, (2) redesigning existing monitoring networks, and (3) expanding existing monitoring networks. The kriging method was utilized to determine groundwater levels at non-monitoring locations for preparing the design data base. The optimization of the groundwater monitoring network had the objectives of (1) minimizing the root mean square error and (2) minimizing the number of wells. The non-dominated sorting genetic algorithm (NSGA-II) was applied to optimize the network. Inverse distance weighting interpolation was used in NSGA-II to estimate the groundwater levels while optimizing network design. Results of the study indicate that the proposed method successfully optimizes the design of groundwater monitoring networks that achieve accuracy and cost-effectiveness.

A Bayesian maximum entropy-based methodology for optimal spatiotemporal design of groundwater monitoring networks.

This paper presents a new methodology for analyzing the spatiotemporal variability of water table levels and redesigning a groundwater level monitoring network (GLMN) using the Bayesian Maximum Entropy (BME) technique and a multi-criteria decision-making approach based on ordered weighted averaging (OWA). The spatial sampling is determined using a hexagonal gridding pattern and a new method, which is proposed to assign a removal priority number to each pre-existing station. To design temporal sampling, a new approach is also applied to consider uncertainty caused by lack of information. In this approach, different time lag values are tested by regarding another source of information, which is simulation result of a numerical groundwater flow model. Furthermore, to incorporate the existing uncertainties in available monitoring data, the flexibility of the BME interpolation technique is taken into account in applying soft data and improving the accuracy of the calculations. To examine the methodology, it is applied to the Dehgolan plain in northwestern Iran. Based on the results, a configuration of 33 monitoring stations for a regular hexagonal grid of side length 3600m is proposed, in which the time lag between samples is equal to 5weeks. Since the variance estimation errors of the BME method are almost identical for redesigned and existing networks, the redesigned monitoring network is more cost-effective and efficient than the existing monitoring network with 52 stations and monthly sampling frequency.

Entropy based groundwater monitoring network design considering spatial distribution of annual recharge

This study explores the inclusion of a groundwater recharge based design objective and the impact it has on the design of optimum groundwater monitoring networks. The study was conducted in the Hamilton, Halton, and Credit Valley regions of Ontario, Canada, in which the existing Ontario Provincial Groundwater Monitoring Network was augmented with additional monitoring wells. The Dual Entropy-Multiobjective Optimization (DEMO) model was used in these analyses. The value of using this design objective is rooted in the information contained within the estimated recharge. Recharge requires knowledge of climate, geomorphology, and geology of the area, thus using this objective function can help account for these physical characteristics. Two sources of groundwater recharge data were examined and compared, the first was calculated using the Precipitation-Runoff Modeling System (PRMS), and the second was an aggregation of recharge found using both the PRMS and Hydrological Simulation Program-Fortran (HSP-F). The entropy functions are used to identify optimal trade-offs between the maximum information content and the minimum shared information between the monitoring wells. The recharge objective will help to quantify hydrological characteristics of the vadose zone, and thus provide more information to the optimization algorithm. Results show that by including recharge as a design objective, the spatial coverage of the monitoring network can be improved. The study also highlights the flexibility of DEMO and its ability to incorporate additional design objectives such as the groundwater recharge.

Multi-objective optimization of long-term groundwater monitoring network design using a probabilistic Pareto genetic algorithm under uncertainty

Optimal design of long term groundwater monitoring (LTGM) network often involves conflicting objectives and substantial uncertainty arising from insufficient hydraulic conductivity (K) data. This study develops a new multi-objective simulation–optimization model involving four objectives: minimizations of (i) the total sampling costs for monitoring contaminant plume, (ii) mass estimation error, (iii) the first moment estimation error, and (iv) the second moment estimation error of the contaminant plume, for LTGM network design problems. Then a new probabilistic Pareto genetic algorithm (PPGA) coupled with the commonly used flow and transport codes, MODFLOW and MT3DMS, is developed to search for the Pareto-optimal solutions to the multi-objective LTGM problems under uncertainty of the K-fields. The PPGA integrates the niched Pareto genetic algorithm with probabilistic Pareto sorting scheme to deal with the uncertainty of objectives caused by the uncertain K-field. Also, the elitist selection strategy, the operation library and the Pareto solution set filter are conducted to improve the diversity and reliability of Pareto-optimal solutions by the PPGA. Furthermore, the sampling strategy of noisy genetic algorithm is adopted to cope with the uncertainty of the K-fields and improve the computational efficiency of the PPGA. In particular, Monte Carlo (MC) analysis is employed to evaluate the effectiveness of the proposed methodology in finding Pareto-optimal sampling network designs of LTGM systems through a two-dimensional hypothetical example and a three-dimensional field application in Indiana (USA). Comprehensive analysis demonstrates that the proposed PPGA can find Pareto optimal solutions with low variability and high reliability and is a promising tool for optimizing multi-objective LTGM network designs under uncertainty.

Review: Simulation-optimization models for the management and monitoring of coastal aquifers

The literature on the application of simulation-optimization approaches for management and monitoring of coastal aquifers is reviewed. Both sharp- and dispersive-interface modeling approaches have been applied in conjunction with optimization algorithms in the past to develop management solutions for saltwater intrusion. Simulation-optimization models based on sharp-interface approximation are often based on the Ghyben-Herzberg relationship and provide an efficient framework for preliminary designs of saltwater-intrusion management schemes. Models based on dispersive-interface numerical models have wider applicability but are challenged by the computational burden involved when applied in the simulation-optimization framework. The use of surrogate models to substitute the physically based model during optimization has been found to be successful in many cases. Scalability is still a challenge for the surrogate modeling approach as the computational advantage accrued is traded-off with the training time required for the surrogate models as the problem size increases. Few studies have attempted to solve stochastic coastal-aquifer management problems considering model prediction uncertainty. Approaches that have been reported in the wider groundwater management literature need to be extended and adapted to address the challenges posed by the stochastic coastal-aquifer management problem. Similarly, while abundant literature is available on simulation-optimization methods for the optimal design of groundwater monitoring networks, applications targeting coastal aquifer systems are rare. Methods to optimize compliance monitoring strategies for coastal aquifers need to be developed considering the importance of monitoring feedback information in improving the management strategies.

Many‐objective groundwater monitoring network design using bias‐aware ensemble Kalman filtering, evolutionary optimization, and visual analytics

This study contributes the Adaptive Strategies for Sampling in Space and Time (ASSIST) framework for improving long‐term groundwater monitoring decisions across space and time while accounting for the influences of systematic model errors (or predictive bias). The new framework combines contaminant flow‐and‐transport modeling, bias‐aware ensemble Kalman filtering (EnKF), many‐objective evolutionary optimization, and visual analytics‐based decision support. The ASSIST framework allows decision makers to forecast the value of investments in new observations for many objectives simultaneously. Information tradeoffs are evaluated using an EnKF to forecast plume transport in space and time in the presence of uncertain and biased model predictions that are conditioned on uncertain measurement data. This study demonstrates the ASSIST framework using a laboratory‐based physical aquifer tracer experiment. In this initial demonstration, the position and frequency of tracer sampling was optimized to (1) minimize monitoring costs, (2) maximize the information provided to the EnKF, (3) minimize failures to detect the tracer, (4) maximize the detection of tracer fluxes, (5) minimize error in quantifying tracer mass, and (6) minimize error in quantifying the centroid of the tracer plume. Our results demonstrate that the forecasting, search, and visualization components of the ASSIST framework represent a significant advance for observation network design that has a strong potential to innovate our characterization, prediction, and management of groundwater systems.

Logic-Based Design of Groundwater Monitoring Network for Redundancy Reduction

A methodology is developed based on an optimization model solution for optimal design of groundwater quality monitoring network. Redundancy in monitoring network results in economic and overall inefficiency of the network. Therefore, redundancy reduction is an important issue in the optimal design of a monitoring network. The developed methodology reduces monitoring redundancy. It incorporates the inverse distance weighting method for spatial interpolation of concentration data. The formulated logic-based mixed-integer linear optimization model is solved using the branch-and-bound algorithm. The proposed methodology is tested for a real world problem. Performance of the proposed methodology is evaluated for different scenarios using available historical concentration data. These performance evaluation results show that the proposed methodology performs satisfactorily when compared with other existing methodologies. These results demonstrate the potential applicability of the proposed methodology for groundwater contaminant monitoring network design, while incorporating reduction in redundancy of monitoring locations.

Comparison of Variance‐Reduction and Space‐Filling Approaches for the Design of Environmental Monitoring Networks

Abstract: This article focuses on the design of groundwater monitoring networks to detect contamination with nitrates from agricultural origin. This is a problem that has been in the minds of the general public, scientists, governmental agencies, and legislators for some time now. If one looks at European statistics, despite still incomplete data, in 13% of the regions the 50 mg/l European water quality standard for drinking water (Drinking Water Directive, 98/83/EC) is exceeded in more than 25% of the monitoring stations. A compliance groundwater nitrate‐monitoring network is developed for a case study in the south of Portugal (Gabbro of Beja aquifer system), using both variance‐reduction and space‐filling approaches. In the first the variance of the error of estimation obtained by ordinary kriging is used, after building a covariance model, and the objective is to minimize the average kriging variance. In the second approach a criterion for the quality of spatial coverage is used, usually based on a metric. The objective here is also usually to minimize a function of the metric (criterion). The search for a solution is made in an iterative manner, by replacing one station, analyzing the result, and deciding whether to keep the solution or not. Due to the enormous number of possible combinations even for small networks, a structured search method, the simulated annealing method, is used to obtain the final network.

Local Hydraulic Gradient Estimator Analysis of Long‐Term Monitoring Networks

Three measurements of head at unique locations form a three-point estimator of the local magnitude and orientation of the hydraulic gradient. The relative head measurement error (RHME) is defined here as the measurement error normalized by the head drop across the three-point estimator. Monte Carlo simulation results show that estimators with base to height ratios between 0.5 and 5.0 and that are large enough to keep the RHME below 0.05 create the most accurate gradient estimates and provide criteria for identifying good estimators. These criteria are applied to an example ground water monitoring network design problem in the Culebra dolomite near the Waste Isolation Pilot Plant repository to both analyze temporal changes and modify and expand the current monitoring network. Limiting the three-point estimators to those that meet the shape and RHME criteria reduces the number of possible estimators by >50% and leads to approximately 1 order of magnitude decrease in the average estimated magnitude of the gradient relative to using all estimators. Application of these criteria also reduces the variability in estimated gradient magnitude and orientation between the two time periods of measurements. Redundant wells in the network are identified by removing each existing well in turn and determining which removals yield the smallest decrease in the number of acceptable estimators. Optimal new well locations are identified by mapping the increase in total number of acceptable estimators for a single new well placed in the study domain.

On the equivalence of kriging and maximum entropy estimators

This study compares kriging and maximum entropy estimators for spatial estimation and monitoring network design. For second-order stationary random fields (a subset of Gaussian fields) the estimators and their associated interpolation error variances are identical. Simple lognormal kriging differs from the lognormal maximum entropy estimator, however, in both mathematical formulation and estimation error variances. Two numerical examples are described that compare the two estimators. Simple lognormal kriging yields systematically higher estimates and smoother interpolation surfaces compared to those produced by the lognormal maximum entropy estimator. The second empirical comparison applies kriging and entropy-based models to the problem of optimizing groundwater monitoring network design, using six alternative objective functions. The maximum entropy-based sampling design approach is shown to be the more computationally efficient of the two.