1,402 research outputs found

    Bayesian estimation of the transmissivity spatial structure from pumping test data

    Get PDF
    Estimating the statistical parameters (mean, variance, and integral scale) that define the spatial structure of the transmissivity or hydraulic conductivity fields is a fundamental step for the accurate prediction of subsurface flow and contaminant transport. In practice, the determination of the spatial structure is a challenge because of spatial heterogeneity and data scarcity. In this paper, we describe a novel approach that uses time drawdown data from multiple pumping tests to determine the transmissivity statistical spatial structure. The method builds on the pumping test interpretation procedure of Copty et al. (2011) (Continuous Derivation method, CD), which uses the time-drawdown data and its time derivative to estimate apparent transmissivity values as a function of radial distance from the pumping well. A Bayesian approach is then used to infer the statistical parameters of the transmissivity field by combining prior information about the parameters and the likelihood function expressed in terms of radially-dependent apparent transmissivities determined from pumping tests. A major advantage of the proposed Bayesian approach is that the likelihood function is readily determined from randomly generated multiple realizations of the transmissivity field, without the need to solve the groundwater flow equation. Applying the method to synthetically-generated pumping test data, we demonstrate that, through a relatively simple procedure, information on the spatial structure of the transmissivity may be inferred from pumping tests data. It is also shown that the prior parameter distribution has a significant influence on the estimation procedure, given the non-uniqueness of the estimation procedure. Results also indicate that the reliability of the estimated transmissivity statistical parameters increases with the number of available pumping tests.Peer ReviewedPostprint (author's final draft

    Development of sustainable groundwater management methodologies to control saltwater intrusion into coastal aquifers with application to a tropical Pacific island country

    Get PDF
    Saltwater intrusion due to the over-exploitation of groundwater in coastal aquifers is a critical challenge facing groundwater-dependent coastal communities throughout the world. Sustainable management of coastal aquifers for maintaining abstracted groundwater quality within permissible salinity limits is regarded as an important groundwater management problem necessitating urgent reliable and optimal management methodologies. This study focuses on the development and evaluation of groundwater salinity prediction tools, coastal aquifer multi-objective management strategies, and adaptive management strategies using new prediction models, coupled simulation-optimization (S/O) models, and monitoring network design, respectively. Predicting the extent of saltwater intrusion into coastal aquifers in response to existing and changing pumping patterns is a prerequisite of any groundwater management framework. This study investigates the feasibility of using support vector machine regression (SVMR), an innovative artificial intelligence-based machine learning algorithm, to predict salinity at monitoring wells in an illustrative aquifer under variable groundwater pumping conditions. For evaluation purposes, the prediction results of SVMR are compared with well-established genetic programming (GP) based surrogate models. The prediction capabilities of the two learning machines are evaluated using several measures to ensure their practicality and generalisation ability. Also, a sensitivity analysis methodology is proposed for assessing the impact of pumping rates on salt concentrations at monitoring locations. The performance evaluations suggest that the predictive capability of SVMR is superior to that of GP models. The sensitivity analysis identifies a subset of the most influential pumping rates, which is used to construct new SVMR surrogate models with improved predictive capabilities. The improved predictive capability and generalisation ability of SVMR models, together with the ability to improve the accuracy of prediction by refining the dataset used for training, make the use of SVMR models more attractive. Coupled S/O models are efficient tools that are used for designing multi-objective coastal aquifer management strategies. This study applies a regional-scale coupled S/O methodology with a Pareto front clustering technique to prescribe optimal groundwater withdrawal patterns from the Bonriki aquifer in the Pacific Island of Kiribati. A numerical simulation model is developed, calibrated and validated using field data from the Bonriki aquifer. For computational feasibility, SVMR surrogate models are trained and tested utilizing input-output datasets generated using the flow and transport numerical simulation model. The developed surrogate models were externally coupled with a multi-objective genetic algorithm optimization (MOGA) model, as a substitute for the numerical model. The study area consisted of freshwater pumping wells for extracting groundwater. Pumping from barrier wells installed along the coastlines is also considered as a management option to hydraulically control saltwater intrusion. The objective of the multi-objective management model was to maximise pumping from production wells and minimize pumping from barrier wells (which provide a hydraulic barrier) to ensure that the water quality at different monitoring locations remains within pre-specified limits. The executed multi-objective coupled S/O model generated 700 Pareto-optimal solutions. Analysing a large set of Pareto-optimal solution is a challenging task for the decision-makers. Hence, the k-means clustering technique was utilized to reduce the large Pareto-optimal solution set and help solve the large-scale saltwater intrusion problem in the Bonriki aquifer. The S/O-based management models have delivered optimal saltwater intrusion management strategies. However, at times, uncertainties in the numerical simulation model due to uncertain aquifer parameters are not incorporated into the management models. The present study explicitly incorporates aquifer parameter uncertainty into a multi-objective management model for the optimal design of groundwater pumping strategies from the unconfined Bonriki aquifer. To achieve computational efficiency and feasibility of the management model, the calibrated numerical simulation model in the S/O model was is replaced with ensembles of SVMR surrogate models. Each SVMR standalone surrogate model in the ensemble is constructed using datasets from different numerical simulation models with different hydraulic conductivity and porosity values. These ensemble SVMR models were coupled to the MOGA model to solve the Bonriki aquifer management problem for ensuring sustainable withdrawal rates that maintain specified salinity limits. The executed optimization model presented a Pareto-front with 600 non-dominated optimal trade-off pumping solutions. The reliability of the management model, established after validation of the optimal solution results, suggests that the implemented constraints of the optimization problem were satisfied; i.e., the salinities at monitoring locations remained within the pre-specified limits. The correct implementation of a prescribed optimal management strategy based on the coupled S/O model is always a concern for decision-makers. The management strategy actually implemented in the field sometimes deviates from the recommended optimal strategy, resulting in field-level deviations. Monitoring such field-level deviations during actual implementation of the recommended optimal management strategy and sequentially updating the strategy using feedback information is an important step towards adaptive management of coastal groundwater resources. In this study, a three-phase adaptive management framework for a coastal aquifer subjected to saltwater intrusion is applied and evaluated for a regional-scale coastal aquifer study area. The methodology adopted includes three sequential components. First, an optimal management strategy (consisting of groundwater extraction from production and barrier wells) is derived and implemented for the optimal management of the aquifer. The implemented management strategy is obtained by solving a homogeneous ensemble-based coupled S/O model. Second, a regional-scale optimal monitoring network is designed for the aquifer system, which considers possible user noncompliance of a recommended management strategy and uncertainty in aquifer parameter estimates. A new monitoring network design is formulated to ensure that candidate monitoring wells are placed at high risk (highly contaminated) locations. In addition, a k-means clustering methodology is utilized to select candidate monitoring wells in areas representative of the entire model domain. Finally, feedback information in the form of salinity measurements at monitoring wells is used to sequentially modify pumping strategies for future time periods in the management horizon. The developed adaptive management framework is evaluated by applying it to the Bonriki aquifer system. Overall, the results of this study suggest that the implemented adaptive management strategy has the potential to address practical implementation issues arising due to user noncompliance, as well as deviations between predicted and actual consequences of implementing a management strategy, and uncertainty in aquifer parameters. The use of ensemble prediction models is known to be more accurate standalone prediction models. The present study develops and utilises homogeneous and heterogeneous ensemble models based on several standalone evolutionary algorithms, including artificial neural networks (ANN), GP, SVMR and Gaussian process regression (GPR). These models are used to predict groundwater salinity in the Bonriki aquifer. Standalone and ensemble prediction models are trained and validated using identical pumping and salinity concentration datasets generated by solving numerical 3D transient density-dependent coastal aquifer flow and transport numerical simulation models. After validation, the ensemble models are used to predict salinity concentration at selected monitoring wells in the modelled aquifer under variable groundwater pumping conditions. The predictive capabilities of the developed ensemble models are quantified using standard statistical procedures. The performance evaluation results suggest that the predictive capabilities of the standalone prediction models (ANN, GP, SVMR and GPR) are comparable to those of the groundwater variable-density flow and salt transport numerical simulation model. However, GPR standalone models had better predictive capabilities than the other standalone models. Also, SVMR and GPR standalone models were more efficient (in terms of computational training time) than other standalone models. In terms of ensemble models, the performance of the homogeneous GPR ensemble model was found to be superior to that of the other homogeneous and heterogeneous ensemble models. Employing data-driven predictive models as replacements for complex groundwater flow and transport models enables the prediction of future scenarios and also helps save computational time, effort and requirements when developing optimal coastal aquifer management strategies based on coupled S/O models. In this study, a new data-driven model, namely Group method for data handling (GMDH) approach is developed and utilized to predict salinity concentration in a coastal aquifer and, simultaneously, determine the most influential input predictor variables (pumping rates) that had the most impact onto the outcomes (salinity at monitoring locations). To confirm the importance of variables, three tests are conducted, in which new GMDH models are constructed using subsets of the original datasets. In TEST 1, new GMDH models are constructed using a set of most influential variables only. In TEST 2, a subset of 20 variables (10 most and 10 least influential variables) are used to develop new GMDH models. In TEST 3, a subset of the least influential variables is used to develop GMDH models. A performance evaluation demonstrates that the GMDH models developed using the entire dataset have reasonable predictive accuracy and efficiency. A comparison of the performance evaluations of the three tests highlights the importance of appropriately selecting input pumping rates when developing predictive models. These results suggest that incorporating the least influential variables decreases model accuracy; thus, only considering the most influential variables in salinity prediction models is beneficial and appropriate. This study also investigated the efficiency and viability of using artificial freshwater recharge (AFR) to increase fresh groundwater pumping rates from production wells. First, the effect of AFR on the inland encroachment of saline water is quantified for existing scenarios. Specifically, groundwater head and salinity differences at monitoring locations before and after artificial recharge are presented. Second, a multi-objective management model incorporating groundwater pumping and AFR is implemented to control groundwater salinization in an illustrative coastal aquifer system. A coupled SVMR-MOGA model is developed for prescribing optimal management strategies that incorporate AFR and groundwater pumping wells. The Pareto-optimal front obtained from the SVMR-MOGA optimization model presents a set of optimal solutions for the sustainable management of the coastal aquifer. The pumping strategies obtained as Pareto-optimal solutions with and without freshwater recharge shows that saltwater intrusion is sensitive to AFR. Also, the hydraulic head lenses created by AFR can be used as one practical option to control saltwater intrusion. The developed 3D saltwater intrusion model, the predictive capabilities of the developed SVMR models, and the feasibility of using the proposed coupled multi-objective SVMR-MOGA optimization model make the proposed methodology potentially suitable for solving large-scale regional saltwater intrusion management problems. Overall, the development and evaluation of various groundwater numerical simulation models, predictive models, multi-objective management strategies and adaptive methodologies will provide decision-makers with tools for the sustainable management of coastal aquifers. It is envisioned that the outcomes of this research will provide useful information to groundwater managers and stakeholders, and offer potential resolutions to policy-makers regarding the sustainable management of groundwater resources. The real-life case study of the Bonriki aquifer presented in this study provides the scientific community with a broader understanding of groundwater resource issues in coastal aquifers and establishes the practical utility of the developed management strategies

    Relative importance of geostatistical and transport models in describing heavily tailed breakthrough curves at the Lauswiesen site

    Get PDF
    We analyze the relative importance of the selection of (1) the geostatistical model depicting the structural heterogeneity of an aquifer, and (2) the basic processes to be included in the conceptual model, to describe the main aspects of solute transport at an experimental site. We focus on the results of a forced-gradient tracer test performed at the “Lauswiesen” experimental site, near Tübingen, Germany. In the experiment, NaBr is injected into a well located 52 m from a pumping well. Multilevel breakthrough curves (BTCs) are measured in the latter. We conceptualize the aquifer as a three-dimensional, doubly stochastic composite medium, where distributions of geomaterials and attributes, e.g., hydraulic conductivity (K) and porosity (ϕ), can be uncertain. Several alternative transport processes are considered: advection, advection–dispersion and/or mass-transfer between mobile and immobile regions. Flow and transport are tackled within a stochastic Monte Carlo framework to describe key features of the experimental BTCs, such as temporal moments, peak time, and pronounced tailing. We find that, regardless the complexity of the conceptual transport model adopted, an adequate description of heterogeneity is crucial for generating alternative equally likely realizations of the system that are consistent with (a) the statistical description of the heterogeneous system, as inferred from the data, and (b) salient features of the depth-averaged breakthrough curve, including preferential paths, slow release of mass particles, and anomalous spreading. While the available geostatistical characterization of heterogeneity can explain most of the integrated behavior of transport (depth-averaged breakthrough curve), not all multilevel BTCs are described with equal success. This suggests that transport models simply based on integrated measurements may not ensure an accurate representation of many of the important features required in three-dimensional transport models

    Development of a sustainable groundwater management strategy and sequential compliance monitoring to control saltwater intrusion in coastal aquifers

    Get PDF
    The coastal areas of the world are characterized by high population densities, an abundance of food, and increased economic activities. These increasing human settlements, subsequent increases in agricultural developments and economic activities demand an increasing amount quantity of freshwater supplies to different sectors. Groundwater in coastal aquifers is one of the most important sources of freshwater supplies. Over exploitation of this coastal groundwater resource results in seawater intrusion and subsequent deterioration of groundwater quality in coastal aquifers. In addition, climate change induced sea level rise, in combination with the effect of excessive groundwater extraction, can accelerate the seawater intrusion. Adequate supply of good quality water to different sectors in coastal areas can be ensured by adoption of a proper management strategy for groundwater extraction. Optimal use of the coastal groundwater resource is one of the best management options, which can be achieved by employing a properly developed optimal groundwater extraction strategy. Coupled simulation-optimization (S-O) approaches are essential tools to obtain the optimal groundwater extraction patterns. This study proposes approaches for developing multiple objective management of coastal aquifers with the aid of barrier extraction wells as hydraulic control measure of saltwater intrusion in multilayered coastal aquifer systems. Therefore, two conflicting objectives of management policy are considered in this research, i.e. maximizing total groundwater extraction for advantageous purposes, and minimizing the total amount of water abstraction from barrier extraction wells. The study also proposes an adaptive management strategy for coastal aquifers by developing a three-dimensional (3-D) monitoring network design. The performance of the proposed methodologies is evaluated by using both an illustrative multilayered coastal aquifer system and a real life coastal aquifer study area. Coupled S-O approach is used as the basic tool to develop a saltwater intrusion management model to obtain the optimal groundwater extraction rates from a combination of feasible solutions on the Pareto optimal front. Simulation of saltwater intrusion processes requires solution of density dependent coupled flow and solute transport numerical simulation models that are computationally intensive. Therefore, computational efficiency in the coupled S-O approach is achieved by using an approximate emulator of the accompanying physical processes of coastal aquifers. These emulators, often known as surrogate models or meta-models, can replace the computationally intensive numerical simulation model in a coupled S-O approach for achieving computational efficiency. A number of meta-models have been developed and compared in this study for integration with the optimization algorithm in order to develop saltwater intrusion management model. Fuzzy Inference System (FIS), Adaptive Neuro Fuzzy Inference System (ANFIS), Multivariate Adaptive Regression Spline (MARS), and Gaussian Process Regression (GPR) based meta-models are developed in the present study for approximating coastal aquifer responses to groundwater extraction. Properly trained and tested meta-models are integrated with a Controlled Elitist Multiple Objective Genetic Algorithm (CEMOGA) within a coupled S-O approach. In each iteration of the optimization algorithm, the meta-models are used to compute the corresponding salinity concentrations for a set of candidate pumping patterns generated by the optimization algorithm. Upon convergence, the non-dominated global optimal solutions are obtained as the Pareto optimal front, which represents a trade-off between the two conflicting objectives of the pumping management problem. It is observed from the solutions of the meta-model based coupled S-O approach that the considered meta-models are capable of producing a Pareto optimal set of solutions quite accurately. However, each meta-modelling approach has distinct advantages over the others when utilized within the integrated S-O approach. Uncertainties in estimating complex flow and solute transport processes in coastal aquifers demand incorporation of the uncertainties related to some of the model parameters. Multidimensional heterogeneity of aquifer properties such as hydraulic conductivity, compressibility, and bulk density are considered as major sources of uncertainty in groundwater modelling system. Other sources of uncertainty are associated with spatial and temporal variability of hydrologic as well as human interventions, e.g. aquifer recharge and transient groundwater extraction patterns. Different realizations of these uncertain model parameters are obtained from different statistical distributions. FIS based meta-models are advanced to a Genetic Algorithm (GA) tuned hybrid FIS model (GA-FIS), to emulate physical processes of coastal aquifers and to evaluate responses of the coastal aquifers to groundwater extraction under groundwater parameter uncertainty. GA is used to tune the FIS parameters in order to obtain the optimal FIS structure. The GA-FIS models thus obtained are linked externally to the CEMOGA in order to derive an optimal pumping management strategy using the coupled S-O approach. The evaluation results show that the proposed saltwater intrusion management model is able to derive reliable optimal groundwater extraction strategies to control saltwater intrusion for the illustrative multilayered coastal aquifer system. The optimal management strategies obtained as solutions of GA-FIS based management models are shown to be reliable and accurate within the specified ranges of values for different realizations of uncertain groundwater parameters. One of the major concerns of the meta-model based integrated S-O approach is the uncertainty associated with the meta-model predictions. These prediction uncertainties, if not addressed properly, may propagate to the optimization procedures, and may deteriorate the optimality of the solutions. A standalone meta-model, when used within an optimal management model, may result in the optimization routine producing actually suboptimal solutions that may undermine the optimality of the groundwater extraction strategies. Therefore, this study proposes an ensemble approach to address the prediction uncertainties of meta-models. Ensemble is an approach to assimilate multiple similar or different algorithms or base learners (emulators). The basic idea of ensemble lies in developing a more reliable and robust prediction tool that incorporates each individual emulator's unique characteristic in order to predict future scenarios. Each individual member of the ensemble contains different input -output mapping functions. Based on their own mapping functions, these individual emulators provide varied predictions on the response variable. Therefore, the combined prediction of the ensemble is likely to be less biased and more robust, reliable, and accurate than that of any of the individual members of the ensemble. Performance of the ensemble meta-models is evaluated using an illustrative coastal aquifer study area. The results indicate that the meta-model based ensemble modelling approach is able to provide reliable solutions for a multilayered coastal aquifer management problem. Relative sea level rise, providing an additional saline water head at the seaside, has a significant impact on an increase in the salinization process of the coastal aquifers. Although excessive groundwater withdrawal is considered as the major cause of saltwater intrusion, relative sea level rise, in combination with the effect of excessive groundwater pumping, can exacerbate the already vulnerable coastal aquifers. This study incorporates the effects of relative sea level rise on the optimized groundwater extraction values for the specified management period. Variation of water concentrations in the tidal river and seasonal fluctuation of river water stage are also incorporated. Three meta-models are developed from the solution results of the numerical simulation model that simulates the coupled flow and solute transport processes in a coastal aquifer system. The results reveal that the proposed meta-models are capable of predicting density dependent coupled flow and solute transport patterns quite accurately. Based on the comparison results, the best meta-model is selected as a computationally cheap substitute of the simulation model in the coupled S-O based saltwater intrusion management model. The performance of the proposed methodology is evaluated for an illustrative multilayered coastal aquifer system in which the effect of climate change induced sea level rise is incorporated for the specified management period. The results show that the proposed saltwater intrusion management model provides acceptable, accurate, and reliable solutions while significantly improving computational efficiency in the coupled S-O methodology. The success of the developed management strategy largely depends on how accurately the prescribed management policy is implemented in real life situations. The actual implementation of a prescribed management strategy often differs from the prescribed planned strategy due to various uncertainties in predicting the consequences, as well as practical constraints, including noncompliance with the prescribed strategy. This results in actual consequences of a management strategy differing from the intended results. To bring the management consequences closer to the intended results, adaptive management strategies can be sequentially modified at different stages of the management horizon using feedback measurements from a deigned monitoring network. This feedback information can be the actual spatial and temporal concentrations resulting from the implementation of actual management strategy. Therefore, field-scale compliance of the developed coastal aquifer management strategy is a crucial aspect of an optimally designed groundwater extraction policy. A 3-D compliance monitoring network design methodology is proposed in this study in order to develop an adaptive and sequentially modified management policy, which aims to improve optimal and justifiable use of groundwater resources in coastal aquifers. In the first step, an ensemble meta-model based multiple objective prescriptive model is developed using a coupled S-O approach in order to derive a set of Pareto optimal groundwater extraction strategies. Prediction uncertainty of meta-models is addressed by utilizing a weighted average ensemble using Set Pair Analysis. In the second step, a monitoring network is designed for evaluating the compliance of the implemented strategies with the prescribed management goals due to possible uncertainties associated with field-scale application of the proposed management policy. Optimal monitoring locations are obtained by maximizing Shannon's entropy between the saltwater concentrations at the selected potential locations. Performance of the proposed 3-D sequential compliance monitoring network design is assessed for an illustrative multilayered coastal aquifer study area. The performance evaluations show that sequential improvements of optimal management strategy are possible by utilizing saltwater concentrations measurements at the proposed optimal compliance monitoring locations. The integrated S-O approach is used to develop a saltwater intrusion management model for a real world coastal aquifer system in the Barguna district of southern Bangladesh. The aquifer processes are simulated by using a 3-D finite element based combined flow and solute transport numerical code. The modelling and management of seawater intrusion processes are performed based on very limited hydrogeological data. The model is calibrated with respect to hydraulic heads for a period of five years from April 2010 to April 2014. The calibrated model is validated for the next three-year period from April 2015 to April 2017. The calibrated and partially validated model is then used within the integrated S-O approach to develop optimal groundwater abstraction patterns to control saltwater intrusion in the study area. Computational efficiency of the management model is achieved by using a MARS based meta-model approximately emulating the combined flow and solute transport processes of the study area. This limited evaluation demonstrates that a planned transient groundwater abstraction strategy, acquired as solution results of a meta-model based integrated S-O approach, is a useful management strategy for optimized water abstraction and saltwater intrusion control. This study shows the capability of the MARS meta-model based integrated S-O approach to solve real-life complex management problems in an efficient manner

    Regional groundwater flow dynamics and residence times in Chaudière-Appalaches, Québec, Canada : insights from numerical simulations

    Get PDF
    Tableau d'honneur de la Faculté des études supérieures et postdoctorales, 2017-2018Dans le cadre du projet PACES III pour la région de Chaudière-Appalaches, situé au sud de la ville de Québec, au Canada, l'étude présente une analyse approfondie de l’influence des dynamiques d’écoulement sur la qualité des eaux souterraines dans un contexte régional. L’écoulement régional, le transport d’âge et l'impact d'une faille sur la qualité de l'eau souterraine sont étudiés par l’entremise de modèles numériques bidimensionels. La combinaison des connaissances hydrogéologiques physiques et chimiques, y compris une analyse des concentrations de ¹⁴C dans les eaux souterraines échantillonnées, a conduit à l’ébauche d'un modèle conceptuel de l’écoulement régional. Ce dernier est mis à l’essais pas l’entremise d’un modèle d'écoulement numérique suivant une ligne d’écoulement régionale dans le plan 2D vertical à l’aide du logiciel FLONET. Le modèle est d'abord calibré à l’aide d’une méthode semi-automatisé qui utilise le logiciel PEST en comparant les charges simulés à la piézométrie régionale, et est validé par la comparaison des flux simulés à la recharge. Bien que le modèle affiche l’existence d’un écoulement régional profond, la région à l’étude apparaît être dominée par des systèmes d'écoulements locaux à des échelles maximales d'environ 5 km, avec un écoulement significatif dans le roc fracturé peu profond. L’écoulement actif se limitant à une profondeur maximale de 40 m à 60 m du roc fracturé, confirme que la géochimie des eaux souterraines échantillonnées à partir de puits résidentiels est susceptible d'être affectée par les eaux faisant parti de l’écoulement intermédiaire et régional. Le transport advectif-dispersif de l'âge est ensuite simulé avec le simulateur de transport TR2 et comparé aux temps de déplacement advectifs le long des lignes d’écoulements et à l'âge ¹⁴C des eaux échantillonnées. Enfin, l’influence de la faille de la Rivière Jacques Cartier sur le contexte hydrogéologique régional est étudiée à travers divers scénarios hypothétiques de perméabilité de faille.As part of the PACES III project in the Chaudière-Appalaches region, south of Quebec City, Quebec, Canada, the study herein presents insights into the extent to which regional groundwater quality is shaped by flow dynamics. In this context, 2D numerical modelling is used to simulate regional flow, transport of groundwater age and the possible influence of a fault on groundwater quality. Combining physical and chemical hydrogeological knowledge, including an analysis of ¹⁴C concentrations in sampled groundwater, leads to the development of a regional conceptual flow model. The conceptual model is tested by representing the system with a two-dimensional numerical flow model oriented in the vertical plane roughly south-north towards the St. Lawrence River using the FLONET code. The model is first calibrated to regional piezometry through a semi-automated workflow using PEST and is then validated with average recharge values. Although some evidence for deeper regional flow exists, the area appears to be dominated by local flow systems on maximum length scales of about 5 km, with significant flow through the shallow fractured sedimentary rock aquifer. This regional scale flow model is also supported by the local hydrogeochemical signatures. Active flow appears contained within the top 40 m to 60 m of the fractured bedrock, which confirms that the geochemical signatures of groundwater sampled from residential wells are likely affected by the slow moving waters of the intermediate and regional flow systems. Advective-dispersive transport of groundwater age is then simulated with the TR2 transport model and compared with advective travel times and sampled ¹⁴C water ages. Finally, the possible role of the Jacques-Cartier River fault on regional flow dynamics is investigated by testing various fault permeability configurations

    Ensemble Kalman filter versus ensemble smoother for assessing hydraulic conductivity via tracer test data assimilation

    Get PDF
    Abstract. Estimating the spatial variability of hydraulic conductivity K in natural aquifers is important for predicting the transport of dissolved compounds. Especially in the nonreactive case, the plume evolution is mainly controlled by the heterogeneity of K. At the local scale, the spatial distribution of K can be inferred by combining the Lagrangian formulation of the transport with a Kalman-filter-based technique and assimilating a sequence of time-lapse concentration C measurements, which, for example, can be evaluated on site through the application of a geophysical method. The objective of this work is to compare the ensemble Kalman filter (EnKF) and the ensemble smoother (ES) capabilities to retrieve the hydraulic conductivity spatial distribution in a groundwater flow and transport modeling framework. The application refers to a two-dimensional synthetic aquifer in which a tracer test is simulated. Moreover, since Kalman-filter-based methods are optimal only if each of the involved variables fit to a Gaussian probability density function (pdf) and since this condition may not be met by some of the flow and transport state variables, issues related to the non-Gaussianity of the variables are analyzed and different transformation of the pdfs are considered in order to evaluate their influence on the performance of the methods. The results show that the EnKF reproduces with good accuracy the hydraulic conductivity field, outperforming the ES regardless of the pdf of the concentrations

    Bayesian estimation of the transmissivity spatial structure from pumping test data

    Get PDF
        Estimating the statistical parameters (mean, variance, and integral scale) that define the spatial structure of the transmissivity or hydraulic conductivity fields is a fundamental step for the accurate prediction of subsurface flow and contaminant transport. In practice, the determination of the spatial structure is a challenge because of spatial heterogeneity and data scarcity. In this paper, we describe a novel approach that uses time drawdown data from multiple pumping tests to determine the transmissivity statistical spatial structure. The method builds on the pumping test interpretation procedure of Copty et al. (2011) (Continuous Derivation method, CD), which uses the time-drawdown data and its time derivative to estimate apparent transmissivity values as a function of radial distance from the pumping well. A Bayesian approach is then used to infer the statistical parameters of the transmissivity field by combining prior information about the parameters and the likelihood function expressed in terms of radially-dependent apparent transmissivities determined from pumping tests. A major advantage of the proposed Bayesian approach is that the likelihood function is readily determined from randomly generated multiple realizations of the transmissivity field, without the need to solve the groundwater flow equation. Applying the method to synthetically-generated pumping test data, we demonstrate that, through a relatively simple procedure, information on the spatial structure of the transmissivity may be inferred from pumping tests data. It is also shown that the prior parameter distribution has a significant influence on the estimation procedure, given the non-uniqueness of the estimation procedure. Results also indicate that the reliability of the estimated transmissivity statistical parameters increases with the number of available pumping tests

    A hierarchy of models for simulating experimental results from a 3D heterogeneous porous medium

    Full text link
    In this work we examine the dispersion of conservative tracers (bromide and fluorescein) in an experimentally-constructed three-dimensional dual-porosity porous medium. The medium is highly heterogeneous (σY2=5.7\sigma_Y^2=5.7), and consists of spherical, low-hydraulic-conductivity inclusions embedded in a high-hydraulic-conductivity matrix. The bi-modal medium was saturated with tracers, and then flushed with tracer-free fluid while the effluent breakthrough curves were measured. The focus for this work is to examine a hierarchy of four models (in the absence of adjustable parameters) with decreasing complexity to assess their ability to accurately represent the measured breakthrough curves. The most information-rich model was (1) a direct numerical simulation of the system in which the geometry, boundary and initial conditions, and medium properties were fully independently characterized experimentally with high fidelity. The reduced models included; (2) a simplified numerical model identical to the fully-resolved direct numerical simulation (DNS) model, but using a domain that was one-tenth the size; (3) an upscaled mobile-immobile model that allowed for a time-dependent mass-transfer coefficient; and, (4) an upscaled mobile-immobile model that assumed a space-time constant mass-transfer coefficient. The results illustrated that all four models provided accurate representations of the experimental breakthrough curves as measured by global RMS error. The primary component of error induced in the upscaled models appeared to arise from the neglect of convection within the inclusions. Interestingly, these results suggested that the conventional convection-dispersion equation, when applied in a way that resolves the heterogeneities, yields models with high fidelity without requiring the imposition of a more complex non-Fickian model.Comment: 27 pages, 9 Figure

    Stochastic Inverse Methods to Identify non-Gaussian Model Parameters in Heterogeneous Aquifers

    Full text link
    La modelación numérica del flujo de agua subterránea y del transporte de masa se está convirtiendo en un criterio de referencia en la actualidad para la evaluación de recursos hídricos y la protección del medio ambiente. Para que las predicciones de los modelos sean fiables, estos deben de estar lo más próximo a la realidad que sea posible. Esta proximidad se adquiere con los métodos inversos, que persiguen la integración de los parámetros medidos y de los estados del sistema observados en la caracterización del acuífero. Se han propuesto varios métodos para resolver el problema inverso en las últimas décadas que se discuten en la tesis. El punto principal de esta tesis es proponer dos métodos inversos estocásticos para la estimación de los parámetros del modelo, cuando estos no se puede describir con una distribución gausiana, por ejemplo, las conductividades hidráulicas mediante la integración de observaciones del estado del sistema, que, en general, tendrán una relación no lineal con los parámetros, por ejemplo, las alturas piezométricas. El primer método es el filtro de Kalman de conjuntos con transformación normal (NS-EnKF) construido sobre la base del filtro de Kalman de conjuntos estándar (EnKF). El EnKF es muy utilizado como una técnica de asimilación de datos en tiempo real debido a sus ventajas, como son la eficiencia y la capacidad de cómputo para evaluar la incertidumbre del modelo. Sin embargo, se sabe que este filtro sólo trabaja de manera óptima cuándo los parámetros del modelo y las variables de estado siguen distribuciones multigausianas. Para ampliar la aplicación del EnKF a vectores de estado no gausianos, tales como los de los acuíferos en formaciones fluvio-deltaicas, el NSEnKF propone aplicar una transformación gausiana univariada. El vector de estado aumentado formado por los parámetros del modelo y las variables de estado se transforman en variables con una distribución marginal gausiana.Zhou ., H. (2011). Stochastic Inverse Methods to Identify non-Gaussian Model Parameters in Heterogeneous Aquifers [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12267Palanci
    corecore