Optimal Groundwater Research Articles

Assessment of the groundwater threshold of desert riparian forest vegetation along the middle and lower reaches of the Tarim River, China

AbstractSurvival of desert riparian vegetation along the middle and lower reaches of the Tarim River in China depends almost entirely on groundwater. The aim of this project was to study the effect of various water table levels on desert riparian forest vegetation and to determine the groundwater threshold under which desert riparian forest vegetation can grow normally. Using data collected from 2 years (2006–2007) of monitoring (groundwater, vegetation plots, and soil profiles) of the middle and lower reaches of the Tarim River, the relationship between vegetation and environmental factors was analysed using detrended canonical correspondence analysis (DCCA). The average depth of the groundwater table at the study sites was divided into six ranges (0–2, 2–4, 4–6, 6–8, 8–10, and >10 m) to compare the influence of groundwater level on species diversity. Using the ecological suitability theory and data from previous studies of several major plant species in the study area, a logarithm normal distribution relation model was built between the growth of plants and the depth of groundwater. The results of DCCA ordination indicated that groundwater depth (GWD) and soil moisture are the crucial environmental factors affecting the distribution of plants. Species diversity was the highest at the 2–4 m level, followed by the 4–6 m level, and then the 0–2 m level. When the groundwater level dropped to below 6 m, species diversity decreased greatly, and the slope of Hill's index (diversity ordering) tended to level off. Thus, the optimum groundwater level for major plant growth is 2–4 m and the threshold GWD is about 6 m. To restore the vegetation and ecosystem of the lower reaches of the Tarim River, the water table must be kept at a minimum of 6 m in depth. It should also be maintained at 2–4 m in the vicinity of the water way and at 4–6 m for the rest of this arid area. Copyright © 2009 John Wiley & Sons, Ltd.

Reciprocal influence of crops and shallow ground water in sandy landscapes of the Inland Pampas

In regions with shallow water tables, ground water may have a positive (water supply) or negative (waterlogging or salinization) impact on crops. Reciprocally, crops can influence ground water, altering water table depth and chemical composition. We quantified these reciprocal influences along natural gradients of groundwater depth in flat sedimentary landscapes of the Inland Pampas occupied by wheat, soybean, and maize during two growing seasons (2006/2007 and 2007/2008). We correlated crop yield and groundwater depth maps at the field level and made direct plant, soil and groundwater observations at the stand level across topographic gradients. Water table level largely accounted for spatial crop yield variation, explaining 20–75% of their variance. An optimum groundwater depth range, where crop yields were highest, was observed for all three crop species analyzed (1.40–2.45 m for maize, 1.20–2.20 m for soybean, and 0.70–1.65 m for wheat). The areas within these optimum bands had yields that were 3.7, 3 and 1.8 times larger than those where the water table was below 4 m for wheat, maize, and soybean, respectively. As groundwater levels become shallower than these depth bands, crop yields declined sharply (∼0.05 kg m −2 on average for every 10 cm increase in water table level), suggesting negative effects of waterlogging, root anoxia and/or salinity. Groundwater levels below these depth bands were associated with gradually declining yields, likely driven by poorer groundwater supply. Crops influenced groundwater levels through their control of recharge and discharge fluxes. The presence of active crops prevented recharge events (sharp water table level rises) observed during rainy periods in fall and spring. Crops consumed ground water generating increasing discharge as the water table depth decreased. This consumption led to rising soil and groundwater salinization towards shallower water table positions as the growing season progressed. The electrical conductivity of ground water for maize at maturity doubled the pre-sowing values (∼2.2 dS m −1 vs. ∼1.1 dS m −1, p < 0.01,) when ground water was above 2-m depth, whereas negligible changes were observed when groundwater depth exceeded 3.5 m. In flat humid landscapes, such as the Inland Pampas, crops and shallow ground water may be closely connected and influence each other through different mechanisms, posing both opportunities and risks for agricultural systems. Understanding these complex interactions could help raise and stabilize yields and provide keys to regulate the labile hydrology of these plains.

Groundwater quality mapping in urban groundwater using GIS

Konya City, located in the central part of Turkey, has grown and urbanized rapidly. A large amount of the water requirement of Konya City is supplied from groundwater. The quality of this groundwater was determined by taking samples from 177 of the wells within the study area. The purposes of this investigation were (1) to provide an overview of present groundwater quality and (2) to determine spatial distribution of groundwater quality parameters such as pH, electrical conductivity, Cl-, SO4(-2), hardness, and NO3- concentrations, and (3) to map groundwater quality in the study area by using GIS and Geostatistics techniques. ArcGIS 9.0 and ArcGIS Geostatistical Analyst were used for generation of various thematic maps and ArcGIS Spatial Analyst to produce the final groundwater quality map. An interpolation technique, ordinary kriging, was used to obtain the spatial distribution of groundwater quality parameters. The final map shows that the southwest of the city has optimum groundwater quality, and, in general, the groundwater quality decreases south to north of the city; 5.03% (21.51 km2) of the total study area is classified to be at the optimum groundwater quality level.

Intelligent Space Tube Optimization for Speeding Ground Water Remedial Design

An innovative Intelligent Space Tube Optimization (ISTO) two-stage approach facilitates solving complex nonlinear flow and contaminant transport management problems. It reduces computational effort of designing optimal ground water remediation systems and strategies for an assumed set of wells. ISTO's stage 1 defines an adaptive mobile space tube that lengthens toward the optimal solution. The space tube has overlapping multidimensional subspaces. Stage 1 generates several strategies within the space tube, trains neural surrogate simulators (NSS) using the limited space tube data, and optimizes using an advanced genetic algorithm (AGA) with NSS. Stage 1 speeds evaluating assumed well locations and combinations. For a large complex plume of solvents and explosives, ISTO stage 1 reaches within 10% of the optimal solution 25% faster than an efficient AGA coupled with comprehensive tabu search (AGCT) does by itself. ISTO input parameters include space tube radius and number of strategies used to train NSS per cycle. Larger radii can speed convergence to optimality for optimizations that achieve it but might increase the number of optimizations reaching it. ISTO stage 2 automatically refines the NSS-AGA stage 1 optimal strategy using heuristic optimization (we used AGCT), without using NSS surrogates. Stage 2 explores the entire solution space. ISTO is applicable for many heuristic optimization settings in which the numerical simulator is computationally intensive, and one would like to reduce that burden.

Potential Well Locations in In Situ Bioremediation Design Using Neural Network Embedded Monte Carlo Approach

An optimal groundwater remediation design problem generally requires determination of the location of extraction and injection wells and their pumping and injection rates once the well locations are selected. According to many researchers, the determination of an optimal well location is even more important than the optimal pumping rate in groundwater remediation problems. The objective of the study is to apply a neural network embedded Monte Carlo approach to determine potential well locations for in situ bioremediation of contaminated groundwater. The methodology developed in this study has three important components: a bioremediation simulation model, an artificial neural network, and an application of Monte Carlo simulations. This method has been further applied to an in situ bioremediation problem, for which data are adopted from the available literature. The results show that the proposed approach can successfully identify potential well locations from a set of preselected well locations, which can ...

Optimum groundwater locations in the northern United Arab Emirates

Due to the increase in urban and agricultural activities in arid regions, the exploration of new locations of possible groundwater discharge and accumulation is required to augment the limited water resources. In order to locate such discharge areas, it is necessary first to identify zones of high recharge potentials. In such an arid region, like the northern United Arab Emirates (UAE), one of the ways to predict areas of potential groundwater recharge is by understanding the hydrological response of its drainage basins to rainfall events. Due to the scarcity of basic hydrological data, a hydrological model driven mainly by information on the physiographic characteristics, drainage network properties (generated from DEM), and surface cover distribution (generated from satellite images) was used to comprehend the dynamics of surface runoff through hydrographs, and hence water loss in the study area. Results show that the northern UAE is drained by 48 drainage basins emerging from the Oman Mountains. Two‐thirds of these basins drain easterly toward the Gulf of Oman, and one‐third drain westerly toward the Arabian Gulf. These basins are found to be structurally controlled by three major fault trends, which are the NE trend (Dibba zone), NW trend (Ham Zone), and WNW trend (Hatta zone). The hydrological response of a basin is correlated with its morphological characteristics. Based on these characteristics, and through the application of a cluster analysis, it was feasible to classify the largest basins in the region into four groundwater potentiality groups in accordance with the magnitude of their peak discharges. From this study, it became evident that the downstream area of the two major basins of Ar‐Rafiah and Limhah, and their vicinities are the most probable sites for groundwater accumulation. The drainage systems of these two basins, especially those controlled by major fault lines, play a vital role in transmitting surface–subsurface rainwater from the Oman Mountains, the recharge zone, into the western desert plain, the discharge zone, where freshwater accumulates underground. The study also revealed that a large volume of groundwater is dissipated into the sea along the eastern coast. A detailed examination of MODIS thermal data supports this by revealing cool surface anomalies issuing from the mountain range toward both the western desert plain and the Gulf of Oman following major rainfall events. Thus, the technique used facilitates the prediction of new locations of optimum groundwater resources in the northern UAE. Such a technique could be adopted, with appropriate modifications, elsewhere in arid regions, where groundwater is restricted and subject to greater complexity.

Bayesian deduction for redundancy detection in groundwater quality monitoring networks

A new methodology for designing a network for monitoring ambient, long‐term groundwater quality is presented in this paper. The methodology is based on a sparse Bayesian learning approach known as a relevance vector machine (RVM) which produces probabilistic predictions that quantify the uncertainty in both the data and the model parameters. A reliable and parsimonious network configuration that is pertinent to the physics of the case study, revealed through understanding of the information content of the available data, is sought through application of the RVM. The methodology has been employed to reduce redundancy in the network for monitoring nitrate (NO3−) in the West Bank Palestinian National Authority aquifers to illustrate the potential for use of RVMs in optimal groundwater monitoring and to explore possible trade‐offs between different monitoring objectives, e.g., monitoring cost versus uncertainty in groundwater. A sparse monitoring network configuration produced by the RVM‐based model indicates that only 32% of the existing monitoring sites in the aquifer are sufficient to characterize the nitrate state. Proof of correctness and accuracy using rigorous statistical tests is presented.

A simulation-based fuzzy chance-constrained programming model for optimal groundwater remediation under uncertainty

In this study a simulation-based fuzzy chance-constrained programming (SFCCP) model is developed based on possibility theory. The model is solved through an indirect search approach which integrates fuzzy simulation, artificial neural network and simulated annealing techniques. This approach has the advantages of: (1) handling simulation and optimization problems under uncertainty associated with fuzzy parameters, (2) providing additional information (i.e. possibility of constraint satisfaction) indicating that how likely one can believe the decision results, (3) alleviating computational burdens in the optimization process, and (4) reducing the chances of being trapped in local optima. The model is applied to a petroleum-contaminated aquifer located in western Canada for supporting the optimal design of groundwater remediation systems. The model solutions provide optimal groundwater pumping rates for the 3, 5 and 10 years of pumping schemes. It is observed that the uncertainty significantly affects the remediation strategies. To mitigate such impacts, additional cost is required either for increased pumping rate or for reinforced site characterization.

Optimizing Ground Water Observation Networks in Irrigation Areas Using Principal Component Analysis

AbstractGround water monitoring networks can provide vital information for sustainable water resources management. This involves the measurement of ground water level, solute concentration, or both. This article deals with the former. It optimizes network distribution of piezometer or data sampling wells to effectively monitor ground water levels under an irrigation region while retaining adequate overall measurement accuracy. This article presents a structured process for applying principal component analysis (PCA) in optimizing a ground water monitoring network in an irrigation area of Australia. The PCA functions, distributed with the MATLAB package, were used to determine relative contributions of individual piezometers in capturing the spatiotemporal variation of ground water levels. Kriging gridding interpolation algorithm was used to render the data surface presentations and determine spatial differences in piezometeric surfaces using different number of data sets. The results show that the overall difference of ground water level between the original piezometer network and the optimized networks after the PCA process was applied is less than 20%, while the total number of piezometers in the optimized network is reduced by 63%, which will save the time and cost to monitor ground water levels in the irrigation area.

Groundwater Management Using Model Reduction via Empirical Orthogonal Functions

This work presents a novel approach for solving groundwater management problems with reduced computational effort. We replace a groundwater flow model governed by a partial differential equation with a simple model governed by an ordinary differential equation. Model reduction is achieved with empirical orthogonal functions, i.e., principal components. Replacement of the full-scale model by a reduced model allows implementation of the embedding approach for optimal groundwater management. Comparing the results obtained with the full-scale simulation model, preliminary analyses show that the reduced model is able to reproduce head variations in the flow domain with good accuracy and, to a certain degree, the sensitivities of head with respect to pumping. A key advantage of the reduced model is that it is simple and easy to solve, and in many instances captures the dominating characteristics of the original model. In view of the many sources of uncertainty influencing groundwater simulation, the accuracy provided by a reduced model may be sufficient for planning purposes. As with other examples of model reduction presented in recent research efforts, the methodology shows promise in presenting general trends, but does not eliminate the need for the original model when more detailed analyses are needed.

Genetic Algorithm for Constrained Optimization Models and Its Application in Groundwater Resources Management

Genetic algorithms (GAs) have been shown to be an efficient tool for the solution of unconstrained optimization problems. In their standard form, GA formulations are “blind” to the constraints of an optimization model when the model involves these constraints. Thus, in GA applications alternative procedures are used to satisfy the constraints of the optimization model. In this study, the method that is utilized in the Complex Algorithm to solve constrained optimization problems is abstracted to develop a repairing procedure for GAs. The proposed procedure, which handles infeasible solutions that may be generated in a standard GA process, is embedded into the conventional GA to yield an improved GA process (IGA) for the solution of optimization problems with equality and inequality constrains. Two numerical examples are included to demonstrate the effectiveness and efficiency of the proposed method for the solution of constrained optimization applications. Finally the IGA is successfully used to develop an optimal groundwater management plan for the Savannah, Ga. region.

Optimal groundwater remediation design considering effects of natural attenuation processes: pumping strategy with enhanced-natural-attenuation

A simulation-optimization model is proposed to investigate the effect of natural attenuation processes on pump-and-treat and to enhance the application efficiency of the remediation method. Numerical experiments under varying retardation and attenuation conditions demonstrate that the efficacy and the feasibility of pump-and treat are considerably sensitive to the retardation factor and the attenuation rates. The severe retarded condition and attenuation are, the more remediation costs are required and even success of the remediation can not be guaranteed. For these conditions, long pumping strategy or enhancement of biodegradability can be cost-effective and safe. These results indicate that optimal remediations with or without considering natural attenuation processes result in quite different pumping schedules and well locations, and this should be noticed in real practices of groundwater remediation design. The enhanced-natural-attenuation (ENA) optimization process developed to overcome some limitations of the pump-and-treat method determines the locations injecting oxygen to attenuate residual contaminants remained in the aquifer after applying the pump-and-treat. The performances of the ENA model with the cost and the contaminant removal are evaluated for three scenarios. Among these scenarios, second scenario designed for each 0.5 years was most suitable to efficiently remove a low concentration of contaminants remained in the aquifer. This optimization model will be helpful to improve the efficiency in contaminated sites applying the pump-and-treat system.

Stochastically Optimal Groundwater Management Considering Land Subsidence

This paper presents a stochastic groundwater management model explicitly considering land subsidence. Through the use of response matrix technique and one-dimensional consolidation equation, a deterministic management model is first developed. By Latin hypercube sampling technique, along with numerical subsurface flow simulation, statistical features of unit response coefficients due to random hydrogeologic parameters, including hydraulic conductivity K and Lame constants and , are quantified. The first-order- variance-estimation method is adopted to analyze the uncertainties of drawdown and land subsidence based on which the concept of chance-constrained programming is applied to transfer the original deterministic management model into its stochastic form. The stochastic management model enables the determination of optimal total pumpage subject to the constraints that drawdown and land subsidence do not exceed the allowable values with a specified reliability. A hypothetical example is utilized to demonstrate the applica- bility of the stochastic model to five cases in which various levels of parameter uncertainty are considered. The results indicate that joint consideration of drawdown and land subsidence is essential, and the proposed stochastic management model can be generally applied for regional groundwater resources management in conjunction with controlling land subsidence.

Optimizing groundwater development strategies by genetic algorithm: a case study for balancing the needs for agricultural irrigation and environmental protection in northern China

Gaoqing Plain is a major agriculture center of Shandong Province in northern China. Over the last 30 years, the diversion of Yellow River water for intensive irrigation in Gaoqing Plain has led to elevation of the water table and increased evaporation, and subsequently, a dramatic increase in salt content in soil and rapid degradation of crop productivity. Optimal strategies have been explored, that will balance the need to extract sufficient groundwater for irrigation (to ease the pressure on diverting Yellow River water) with the need to improve the local environment by appropriately lowering the water table. Two simulation-optimization models have been formulated and a genetic algorithm (GA) is applied to search for the optimal groundwater development strategies in Gaoqing Plain, while keeping the adverse environmental impacts in check. Compared with the trial-and-error approach of previous studies, the optimization results demonstrate that using an optimization model coupled with a GA search is both effective and efficient. The optimal solutions identified by the GA will provide Gaoqing Plain with the blueprints for developing sustainable groundwater abstraction plans to support local economic development and improve its environmental quality.

Dynamic Optimal Monitoring Network Design for Transient Transport of Pollutants in Groundwater Aquifers

Optimal groundwater pollution monitoring network design models are developed to prescribe optimal and efficient sampling locations for detecting pollution in groundwater aquifers. The developed methodology incorporates a two dimensional flow and transport simulation model to simulate the pollutant concentrations in the study area. Different realizations of the pollutant plume are randomly generated by incorporating the uncertainty in both source and aquifer parameters. These concentration realizations are incorporated in the optimal monitoring network design models. Two different objectives are considered separately. The first objective function minimizes the summation of unmonitored concentrations at different potential monitoring locations. This objective function in effect minimizes the probability of not monitoring the pollutant concentrations at those locations where the probable concentration value is large. Although this probability is not explicitly incorporated in the model, a surrogate form of this objective is included as the objective function. The second objective function considered is the minimization of estimation variances of pollutant concentrations at various unmonitored locations. This objective results in a design that chooses optimal monitoring locations where the uncertainties in simulated concentrations are large. The developed optimization models are solved using Genetic Algorithm. The variances of estimated concentrations at potential monitoring locations are computed using the geostatistical tool, kriging. The designed monitoring network is dynamic in nature, as it provides time varying network designs for different management periods, to account for the transient pollutant plumes. Such a design can eliminate temporal redundancy and is therefore, economically more efficient. The optimal design incorporates budgetary constraints in the form of limits on the number of monitoring wells installed in any particular management period. The solution results are evaluated for an illustrative study area comprising of a hypothetical aquifer. The performance evaluation results establish the potential applicability of the proposed methodology for optimal design of the dynamic monitoring network for detection and monitoring of pollutant plumes in contaminated aquifers.

Efficient Groundwater Remediation System Design Subject to Uncertainty Using Robust Optimization

Many groundwater remediation designs for contaminant plume containment are developed using mathematically based groundwater flow models. These mathematical models are most effective as predictive tools when the parameters that govern groundwater flow are known with a high degree of certainty. The hydraulic conductivity of an aquifer, however, is uncertain, and so remediation designs developed using models employing one realization of the hydraulic conductivity field have an associated risk of failure of plume containment. To account for model uncertainty attributable to hydraulic conductivity in determining an optimal groundwater remediation design for plume containment, a method of optimization called robust optimization is utilized. This method of optimization is a multiscenario approach whereby multiple hydraulic conductivity fields are examined simultaneously. By examining these fields simultaneously, the variability of the uncertainty is included in the model. To increase the efficiency of the robust...

Response of groundwater chemistry to water deliveries in the lower reaches of Tarim River, Northwest China

In this paper, we analysed the monitored data from nine groundwater-monitoring transects in the lower reaches of Tarim River during the five times of stream water deliveries to the river transect where the stream flow ceased. The results showed that the groundwater depth in the lower reaches of Tarim River rose from −9.30 m before the conveyances to −8.17 and −6.50 m after the first and second conveyances, −5.81 and −6.00 m after the third and fourth the conveyance, and −4.73 m after the fifth. The horizontal extent of groundwater recharge was gradually enlarged along both sides of the channel of conveyance, i.e., from 250 m in width after the first conveyance to 1,050 m away from the channel after the fourth delivery. With the rising groundwater level, the concentrations of major anions Cl−, SO 4 2− and cations Ca2+, Mg2+, Na+, as well as total dissolved solids (TDS) in groundwater underwent a significant change. The spatial variations in groundwater chemistry indicated that the groundwater chemistry at the transect near Daxihaizi Reservoir changed earlier than that farther from it. In the same transect, the chemical variations were earlier in the monitoring well close to watercourse than that farther away from the stream. In general, the concentration of the major ions and TDS at each monitoring well increased remarkably when the water delivery started, and decreased with the continued water delivery, and then increased once again at the end of the study period. Hence, the whole study period may be divided into three stages: the initial stage, the intermediate stage and the later stage. According to the three stages of groundwater chemistry reaction to water delivery and the relationships between groundwater chemical properties and groundwater depths, we educe that under the situation of water delivery, the optimum groundwater depth in the lower reaches of the Tarim River should be −5 m.

Groundwater influence on alpine stream ecosystems

Summary1. Spatial and temporal variability of relative snow‐melt, glacier‐melt and groundwater contributions to streams play important roles in shaping alpine freshwater ecosystems. Although meltwater (particularly glacier‐fed) streams have received much attention in recent years, the influence of groundwater on alpine freshwater ecosystems remains poorly understood.2. This study tested the hypotheses that increased groundwater contributions to meltwater‐dominated alpine streams would yield increases in water temperature, channel stability, electrical conductivity and particulate organic matter (POM) and decreases in suspended sediment concentration (SSC). These more favourable habitat conditions were hypothesised to result in increased macroinvertebrate abundance and diversity.3. Groundwater contributions, physicochemical habitat variables and benthic macroinvertebrates were sampled throughout the 2002 and 2003 summer‐melt seasons in three streams in the French Pyrénées.4. Increased groundwater contributions were significantly correlated with higher discharge, water temperature, electrical conductivity, POM and channel stability, but lower SSC.5. Macroinvertebrate total abundance, taxonomic richness, number of Ephemeroptera, Plecoptera and Trichoptera genera, and per cent Plecoptera all increased significantly with greater groundwater contributions to streamflow. However, beta diversity and Trichoptera relative abundance decreased.6. Abundance of most macroinvertebrate taxa was highest under groundwater‐dominated conditions but a gradient of optimum groundwater preferences was evident across all taxa. Some taxa were found only where groundwater contributions were low (i.e. in predominantly meltwater‐fed streams).7. This study provides evidence that water source, physicochemical habitat and stream biota are strongly linked. Therefore, an interdisciplinary approach is necessary for future studies aiming to develop conservation strategies or predict the response of alpine river ecosystems to global climate change.

Optimum control of the ground-water level with account for a rain or snow fall-out

Optimum ground-water conditions of a drained mass of a polder system having one conducting channel were simulated with account for a rain or snow fall-out. The calculations were conducted with the use of an invariant (independent of the number of conducting open channels and their configuration) nonstationary mathematical polder system, involving a strategy for control of the ground-water level in a drained mass (a system of Saint-Venant differential equations for an open channel, a two-dimensional Boussineq equation, and a differential equation for the transfer of water in drains were simultaneously solved).

PGO: A parallel computing platform for global optimization based on genetic algorithm

This paper presents the design, architecture and implementation of a general parallel computing platform, termed PGO, based on the Genetic Algorithm (GA) for global optimization. PGO provides an efficient and easy-to-use framework for parallelizing the global optimization procedure for general scientific modeling and simulation processes. Along with a core optimization kernel built on a GA, PGO also includes a general input generator and an output extractor that can facilitate its easy integration with various scientific computing tasks. In this paper, we demonstrate the efficiency and versatility of PGO with two different applications: (1) the parallelization of a large scale parameter estimation problem associated with modeling water flow in a heterogeneous deep vadose zone; (2) the parallelization of a complex simulation-optimization procedure for searching for an optimal groundwater remediation design. PGO is developed as an open source code, and is independent of the computer operating system. It has been tested in a heterogeneous computing environment consisting of Solaris 9, Fedora Core 2 Linux, and Microsoft Windows machines, and is freely available for download from http://grid.scut.edu.cn/PGO/.