GMS-MODFLOW application in the investigation of groundwater potential in Concepcion, Tarlac, Philippines

This study used GMS-Modflow to investigate the ten-year groundwater potential in Concepcion, Tarlac. This region in Central Luzon, Philippines, with limited surface water, depends on groundwater as its primary freshwater source. The water demand projection estimated an increase of 38.5% from 2020 to 2030; hence, higher groundwater abstraction is perceived in the next ten years. To deviate from the risk associated with reliance on groundwater, this study, through GMS-MODFLOW, developed a groundwater model to mimic the aquifer’s current condition and investigated its behavior in response to future spatial and temporal variables. The simulation results generally showed a sustainable groundwater supply in Concepcion, Tarlac, for the next ten years, with no significant decline in hydraulic heads.

Monitoring Groundwater Quality using Probability Distribution in Gwale, Kano state, Nigeria

Groundwater is the water present beneath the earth’s surface in soil pore spaces and the fractures of rock formations. Establishing a probability distribution that provides a good fit to groundwater quality has recently become a topic of interest in the fields of hydrology, meteorology among others. In this paper, three groundwater datasets including calcium, magnesium, and chloride are fitted to the normal, lognormal, gamma, Weibull, logistic, and log-logistic distributions to select the best groundwater model. The measures of goodness of fits such as the Akaike information criterion (AIC), Bayesian information criterion (BIC), and log-likelihood are computed to compare the fitted models. The results show that the gamma distribution gives better fits for calcium and magnesium datasets while the lognormal distribution provides a better fit for the chloride dataset than other competing models. This research describes an application of probability distributions and the best-fitted distribution to a practical problem involving groundwater data analysis. By assuming the distribution of data, analysts can utilize the characteristics of the distribution to make predictions on outcomes.

Conceptualization and development of multi-layered groundwater model in transient condition

Effective management of water resource is essential in arid and semi-arid areas of India. In Bihar, for drinking purpose humans, livestock is dependent on the groundwater as well as in agricultural areas groundwater plays an important role in irrigation directly or indirectly. There is rise in the groundwater demand due to rapid population increase and fast industrialization. To meet this groundwater demand, excessive withdrawal of groundwater is a point of concern due to limited storage of it. Assessment of the groundwater was done by preparing a numerical model of the groundwater flow. This model is capable of solving large groundwater problems and associated complexity with it. In this study, a transient multi-layered groundwater flow model was conceptualized and developed for the Koshi River basin. In north Bihar plains, the Koshi River is one of the biggest tributaries of the Ganga River system. Koshi originates from the lower part of Tibet and joins the Ganga River in Katihar district, Bihar, India. After model development, calibration of the model was also done, by considering three model parameters, to represent the actual field conditions. For validation of the model, fifteen observation wells have been selected in the area. With the help of observation well data, computed and observed heads were compared. Comparison results have been found to be encouraging and the computed groundwater head matched with the observed water head to a realistic level of accuracy. Developed groundwater model is used to predict the groundwater head and flow budget in the concerned area. The study revealed that groundwater modeling is an important method for knowing the behavior of aquifer systems and to detect groundwater head under different varying hydrological stresses. This type of study will be beneficial for the hydrologist and water resource engineers to predict the groundwater flow behavior, before implementing any project or to implement a correction scheme.

Development of a Numerical Multi-Layered Groundwater Model to Simulate Inter-Aquifer Water Exchange in Shelby County, Tennessee

Inter-aquifer water exchange between the shallow and Memphis aquifers in Shelby County, Tennessee may pose a contamination threat due to the downward migration of younger, poor quality groundwater into deeper, more pristine aquifer. Discontinuities (breaches) in the upper Claiborne confining unit (UCCU) allow for leakage into the Memphis aquifer, a sand-dominated aquifer that provides about 95% of the groundwater used in the Memphis area. This study created a multi-layered 3D groundwater model for Shelby County using the United States Geological Survey’s MODFLOW-NWT program to evaluate water exchange for a simulation period from January 2005 to December 2016. Results indicate an overall leakage through the UCCU of 61 m3/min into the Memphis aquifer in Shelby County, accounting for 10% of its water budget inflow, with localized areas experiencing as much as 20% water exchange. As young water tends to stay in the upper part of the Memphis aquifer, water budget assessment for the upper 60 m of the Memphis aquifer revealed leakage representing 29% of the zone inflow, and as much as 53% in certain areas. More localized studies must be conducted to understand the location, characteristics, and orientation of the confining unit breaches, as well as the inter-aquifer water exchange.

Minimizing Errors in the Prediction of Water Levels Using Kriging Technique in Residuals of the Groundwater Model (MODFLOW)

Groundwater monitoring and water level predictions have been a challenging issue due to the complexity of groundwater movement. Simplified numerical simulation models have been used to represent the groundwater system; these models however only provide the conservative approximation of the system and may not always capture the local variations. Several other efforts such as coupling groundwater models with hydrological models and using geostatistical methods are being practiced to accurately predict the groundwater levels. In this study, we present a novel application of geostatistical tool on residuals of groundwater model. Kriging method was applied on the residuals of the numerical model (MODFLOW) generated by the TWDB (Texas Water Development Board) for the Edwards-Trinity (Plateau) aquifer. The study was done for the years 1995 through 2000 where 90% of the observation data was used for model simulation followed by cross-validation with the remaining 10% of the observations. The kriging method reduced the average absolute error of approximately 31 m (for MODFLOW simulation) to less than 5 m. Also, the residuals’ average standard error reduced from 9.7 to 4.7. This implies that the mean value of residuals over entire period can be a good estimation for each year separately. The use of kriging technique thus can provide with improved monitoring of groundwater levels resulting in more accurate potentiometric surface maps.

The influence of layer and voxel geological modelling strategy on groundwater modelling results

Reliable groundwater model predictions are dependent on representative models of the geological environment, which can be modelled using several different techniques. In order to inform the choice of the geological modelling technique, the differences between a layer modelling approach and a voxel modelling approach were analyzed. The layer model consist of stratigraphically ordered surfaces, while the voxel model consist of a structured mesh of volumetric pixels. Groundwater models based on the two models were developed to investigate their impact on groundwater model predictions. The study was conducted in the relatively data-dense area Egebjerg, Denmark, where both a layer model and a voxel model has been developed based on the same data and geological conceptualization. The characteristics of the two methodologies for developing the geological models were shown to have a direct impact on the resulting models. The differences between the layer and the voxel models were however shown to be diverse and not related to larger conceptual elements with few exceptions. The analysis showed that the geological modelling approaches had an influence on preferred parameter values and thereby groundwater model predictions of hydraulic head, groundwater budget terms and particle tracking results. A significance test taking into account the predictive distributions showed that for many predictions the differences between the models were significant. The results suggest that the geological modelling strategy has an influence on groundwater model predictions even if based on the same geological conceptualization.

Exploring the Adequacy of Steady-State-Only Calibration

This paper explores the adequacy of steady-state-only calibration as a precursor to use of a groundwater model for decision-support. First, it reviews metrics by which a decision-support model should be judged. On the basis of these metrics, it establishes the shortcomings that a decision-support model may incur through foregoing transient calibration. These are 1) failure to reduce the uncertainties of management-salient model predictions to the extent that available data allows, and 2) creation of unquantifiable bias in management-salient predictions. Two methodologies for quantification of these deficiencies are proposed. The first of these addresses uncertainty reduction. This is relatively easy to implement, as it requires only that sensitivities of pertinent model outputs to a model’s parameters be calculated. The second methodology addresses predictive bias. Implementation of this second methodology is more expensive as it requires repeated calibration of a steady state model against stochastic realizations of a transient model.These methods are demonstrated using a synthetic case which explores the viability of steady-state-only calibration of models deployed to examine the impacts of pumping on stream flows and groundwater levels. It is demonstrated that, for some predictions of management interest, steady-state-only calibration is more than sufficient for this kind of decision-support modelling.

Constructing robust and efficient experimental designs in groundwater modeling using a Galerkin method, proper orthogonal decomposition, and metaheuristic algorithms

Estimating parameters accurately in groundwater models for aquifers is challenging because the models are non-explicit solutions of complex partial differential equations. Modern research methods, such as Monte Carlo methods and metaheuristic algorithms, for searching an efficient design to estimate model parameters require hundreds, if not thousands of model calls, making the computational cost prohibitive. One method to circumvent the problem and gain valuable insight on the behavior of groundwater is to first apply a Galerkin method and convert the system of partial differential equations governing the flow to a discrete problem and then use a Proper Orthogonal Decomposition to project the high-dimensional model space of the original groundwater model to create a reduced groundwater model with much lower dimensions. The reduced model can be solved several orders of magnitude faster than the full model and able to provide an accurate estimate of the full model. The task is still challenging because the optimization problem is non-convex, non-differentiable and there are continuous variables and integer-valued variables to optimize. Following convention, heuristic algorithms and a combination is used search to find efficient designs for the reduced groundwater model using various optimality criteria. The main goals are to introduce new design criteria and the concept of design efficiency for experimental design research in hydrology. The two criteria have good utility but interestingly, do not seem to have been implemented in hydrology. In addition, design efficiency is introduced. Design efficiency is a method to assess how robust a design is under a change of criteria. The latter is an important issue because the design criterion may be subjectively selected and it is well known that an optimal design can perform poorly under another criterion. It is thus desirable that the implemented design has relatively high efficiencies under a few criteria. As applications, two heuristic algorithms are used to find optimal designs for a small synthetic aquifer design problem and a design problem for a large-scale groundwater model and assess their robustness properties to other optimality criteria. The results show the proof of concept is workable for finding a more informed and efficient model-based design for a water resource study.