Hydrogeological Uncertainty Research Articles

Insights from electrical resistivity tomography on the hydrogeological interaction between sand dams and the weathered basement aquifer

Sand dams, composed of recent alluvial aquifers behind concrete dam walls, are a water management technique in drylands. However, their level of hydraulic connectivity with their surrounding weathered basement aquifer is debated. This study aims to constrain this hydrogeological uncertainty in order to better understand their ability to meet water needs and improve dryland water security. The study is the first to use 2D geophysics (Electrical Resistivity Tomography) to provide evidence of seepage from sand dams at three mature and three newly built sites. A generally greater hydraulic connectivity was found between sand dams and their surrounding aquifer than has been assumed in some previous studies, with sites providing at least some local recharge rather than existing as isolated storage structures. This improved understanding is beneficial for both site selection and the performance of sand dams and can help ensure that maximum benefits are derived from the construction of a sand dam depending on its intended purpose.

A Novel Approach for Optimum Conjunctive Use Management of Groundwater and Surface Water Resources under Uncertainty

Uncertainty in determining optimum conjunctive water use lies not only on variability of hydrological cycle and climate but also on lack of adequate data and perfect knowledge about groundwater-surface water system interactions, errors in historic data and inherent variability of system parameters both in space and time. Simulation-optimization models are used for conjunctive water use management under uncertain conditions. However, direct application of such approach whereby all realizations are considered at every-iteration of the optimization process leads to a highly computational time-consuming optimization problem as the number of realizations increases. Hence, this study proposes a novel approach—a Retrospective Optimization Approximation (ROA) approach. In this approach, a simulation model was used to determine aquifer system responses (draw-downs) which were assembled as response matrices and incorporated in the optimization model (procedure) as coefficients in the constraints. The sample optimization sub-problems generated, were solved and analyzed through ROA-Active-Set procedure implemented under MATLAB code. The ROA-Active Set procedure solves and evaluates a sequence of sample path optimization sub-problems in an increasing number of realizations. The methodology was applied to a real-world conjunctive water use management problem found in Great Letaba River basin, South Africa. In the River basin, surface water source contributes 87% of the existing un-optimized total conjunctive water use withdrawal rate (6512.04 m3/day) and the remaining 13% is contributed by groundwater source. Through ROA approach, results indicate that the optimum percentages contribution of the surface and subsurface sources to the total water demand are 58% and 42% respectively. This implies that the existing percentage contribution can be increased or reduced by ±29% that is groundwater source can be increased by 29% while the surface water source contribution can be reduced by 29%. This reveals that the existing conjunctive water use practice is unsustainable wherein surface water system is overstressed while groundwater system is under-utilized. Through k-means sampling technique ROA-Active Set procedure was able to attain a converged maximum expected total optimum conjunctive water use withdrawal rate of 4.35 × 104 m3/day within a relatively few numbers of iterations (6 to 8 iterations) in about 2.30 Hrs. In conclusion, results demonstrated that ROA approach is capable of managing real-world regional aquifers sustainable conjunctive water use practice under hydro-geological uncertainty conditions.

Risk analysis framework for the optimum remediation of a contaminated aquifer under uncertainty: application in Lake Karla aquifer, Thessaly, Greece

A risk analysis framework is proposed for the optimum remediation of a contaminated aquifer under hydrogeological uncertainty. The limited information and the spatial variation of hydraulic conductivity in a real-world large-scale aquifer create uncertain conditions for decision-making when remediation schemes ought to be accompanied by the minimum possibility of failure. The primary concern is focused on safeguarding public health when groundwater is used for urban drinking purposes from a contaminated aquifer. The proposed framework is based on the conjunctive use of stochastic simulation–optimization modelling followed up by a risk analysis application on remediation trade-offs. The framework includes three main steps/procedures: (i) the model formulation of multiple realizations of groundwater flow and contaminant transport, (ii) the optimal positioning and operation of the clean-up wells determined by the method of stochastic optimization, and (iii) the risk analysis of the optimum remediation strategies through a proposed decision model, so as the one with the minimum cost and risk of failure is chosen as the most appropriate. The proposed framework is tested for two scenarios of nitrogen fertilizer application in the cultivated areas. The strategic target is the groundwater nitrate concentration minimization in an area where exceedances of nitrate concentrations have been observed and water supply wells have been operating for the last twenty years satisfying domestic needs. The results demonstrate that, when decision-making is under hydrogeological uncertainty, the combined use of stochastic optimization and risk-based decision analysis can commend the remediation strategy with the minimum cost and the highest possibility of success.

Investigation of the nature of suspended matter under the influence of the facies-genetic characteristics of bottom sediments

The purpose of this study is to obtain a mathematical model to assess the influence of the facies and genetic characteristics of bottom sediments on the distribution of suspended matter during dredging.The addressed problem is associated with the detrimental effect of bottom sediments during hydraulic works on the ecological state of a water body. An increase in the level of technogenic turbidity leads to redistribution of the thermal conductivity of water masses and corresponding deterioration in the habitat of aquatic organisms. The development of a mathematical model for the distribution of suspended matter is an urgent task, the solution of which will allow us to analyze the area of distribution of each of the facies of bottom sediment during dredging operations.A mathematical model of the distribution of facies of suspended matter during dredging operations based on the finite difference method has been developed. Software for modeling and visual representation of the process of propagation of facies of suspended matter in the water space of the Gorin River during dredging operations has been developed using C# software, Visual Studio software development environment.The scientific novelty of the performed work is a qualitative and quantitative assessment of the distribution of suspended solids during dredging operations under conditions of hydrogeological uncertainty.

Uncertain Waters: Participatory groundwater modelling in Chicago’s suburbs

Groundwater exists in underground aquifers and is largely hidden and intangible to water users. As such, groundwater models are one of the main vehicles through which groundwater is made legible. They are critical for water supply planning purposes. However, models are imperfect representations of limited data and contain much uncertainty, posing challenges for the water supply planning process. In this paper, we draw on a case from the Greater Chicago area to examine efforts by the authors and the Illinois State Water Survey to engage with local water managers to develop future water supply scenarios. Much of this area has been dependent upon the Cambrian-Ordovician Aquifer System for over 150 years. Over this period, water levels have declined by over 300 m and aquifers are expected to be unviable by 2030. Here we advance the growing field of participatory groundwater modelling (PGM) to identify forms of uncertainty and their influence on understandings of water supply and risk perceptions of depletion. Conceptually, we draw on the idea of models as world builders, where uncertainties are elucidated through knowledge production in the act of model building, while model development is simultaneously influenced by expectations, beliefs, and ambiguity surrounding those using the models. Through planning meetings and focus group discussions between groundwater modelers and water supply stakeholders, we identify four forms of interconnected uncertainty that hinder planning efforts: 1) hydrogeologic uncertainty, 2) modelling uncertainty; 3) water demand uncertainty; and 4) urban planning uncertainty. We describe our PGM efforts to reduce uncertainty and find stakeholder perceptions are as important as model uncertainties in water management decisions. Participatory modelling is effective in reducing and clarifying these four forms of uncertainty, particularly applied to short-term management decisions in a rapidly changing system. We conclude that future participatory modelling efforts need to focus on reducing communication barriers between scientists and local users.

Integration of discrete fracture networks and flow simulator for quantification of hydrogeological uncertainty

In this study, a discrete fracture network model (DFN) and groundwater flow simulation were applied to a fractured aquifer of an open-pit mine. Conditional simulation of the fracture systems was developed to quantify and evaluate the uncertainty of geological structures and to predict possible hydrogeological risks associated with these uncertainties. The method used was based on the statistical characterization and simulation of spatial distribution scenarios of fracture lengths, directions and openings, as well as their influence on water flow behavior. The spatial configuration of the structures was generated using Poisson processes, while the lengths and angles were generated by Gaussian simulation. Flow simulation was performed with Modflow software. The resulting scenarios honored field data and quantified and evaluated the uncertainty associated with fracture distribution. In addition, the study was able to demonstrate the practical aspects of the proposed simulation method, which can then be applied to increase the planning and operational effectiveness of open-pit mines.

What We Talk About When We Talk About Uncertainty. Toward a Unified, Data-Driven Framework for Uncertainty Characterization in Hydrogeology

In this manuscript, we compare and discuss different frameworks for hydrogeological uncertainty analysis. Since uncertainty is a property of knowledge, we base this comparison on purely epistemological concepts. In a detailed comparison between different candidates, we make the case for Bayesianism, i.e., the framework of reasoning about uncertainty using probability theory. We motivate the use of Bayesian tools, shortly explain the properties of Bayesian inference, prediction and decision and identify the most pressing current challenges of this framework. In hydrogeology, these challenges are the derivation of prior distributions for the parametric uncertainty, typically hydraulic conductivity values, as well as the most relevant paradigm for generating subsurface structures for assessing the structural uncertainty. We present the most commonly used paradigms and give detailed advice on two specific paradigms; Gaussian multivariate random fields as well as multiple-point statistics, both of which have benefits and drawbacks. Without settling for either of these paradigms, we identify the lack of open-access data repositories as the most pressing current impediment for the advancement of data-driven uncertainty analysis. We detail the shortcomings of the current situation and describe a number of steps which could foster the application of both the Gaussian as well as the multiple-point paradigm. We close the manuscript with a call for a community-wide initiative to create this necessary support.

Fuzzy-based conflict resolution management of groundwater in-situ bioremediation under hydrogeological uncertainty

In this research study, a fuzzy multi-objective optimization methodology is proposed for in-situ groundwater bioremediation utilizing the Graph Model for Conflict Resolution. In the current model, uncertainties in hydraulic conductivity and the ratio of transverse to longitudinal dispersivity of contaminants are considered using the Fuzzy Transformation Method (FTM). First, the BIOPLUME III simulation model is linked with a Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization model to optimize a groundwater bioremediation system regarding conflicting viewpoints of decision makers. Then, the hydrogeological uncertainties of the groundwater bioremediation system are included in the proposed methodology using FTM. The three main objectives of the in-situ bioremediation optimization model are overall cost (well installation, treatment or pumping, and facility capital), the sum of contaminant concentration violating any standards, and contaminant plume fragmentation, which need to be minimized based on stakeholders’ preferences. Subsequently, GMCR II is utilized to resolve any conflicts between the perspectives of the stakeholders to achieve a compromise solution. The performance assessment results represent the ability of the proposed methodology for optimal in-situ groundwater bioremediation. Results show that the minimum fuzzy interval is 32.5%, and pertains to the overall cost of the bioremediation system in fuzzy α-cut levels of 0 and 0.5. Conversely, the maximum fuzzy interval is related to contaminant plume fragmentation in fuzzy α-cut levels of 0 and 0.5, and was found to be 95.1%.

Application of a link simulation optimization model utilizing quantification of hydrogeologic uncertainty to characterize unknown groundwater contaminant sources

In existing groundwater contamination source characterization methodologies, simulation models estimate the contamination concentration in the study area. In order to obtain reliable solutions, it is essential to provide the simulation models with reliable hydrogeological properties. In real-life scenarios often high level of uncertainty and variability is associated with the hydrogeological properties. This study focuses on quantifying the hydrogeological parameter uncertainty to enhance the accuracy of identifying contamination release histories. Tracer experiment results at the Eastlakes Experimental Site, located in Botany Sands Aquifer, in New South Wales, Australia, are utilized to examine the performance and potential applicability of the methodology. In the selected study area, the hydrogeological heterogeneity in the microscopic scale, specifically the hydraulic conductivity, has substantial effect on the transport of pollutants. Among available tracer information, Bromide is studied as a conservative contaminant. Using possible realizations of the flow field, a coefficient of confidence (COC) is calculated for each field monitoring locations and times. Higher COC implies that the result of simulation models at that specific monitoring location and time is more reliable than other contaminant concentration data. Therefore, the optimization model should emphasise matching the corresponding estimated and observed contamination concentrations to accurately identify the contaminant release locations and histories. The linked simulation–optimisation method is utilised to optimally characterise the Bromide sources. Performance evaluation results demonstrate that the proposed methodology recovers pollution source characteristics more accurately compared to the methodology which does not consider the effect of hydrogeological parameter uncertainty.

Impact of Hydrogeological Uncertainty on Estimation of Environmental Risks Posed by Hydrocarbon Transportation Networks

AbstractUbiquitous hydrogeological uncertainty undermines the veracity of quantitative predictions of soil and groundwater contamination due to accidental hydrocarbon spills from onshore pipelines. Such predictions, therefore, must be accompanied by quantification of predictive uncertainty, especially when they are used for environmental risk assessment. We quantify the impact of parametric uncertainty on quantitative forecasting of temporal evolution of two key risk indices, volumes of unsaturated and saturated soil contaminated by a surface spill of light nonaqueous‐phase liquids. This is accomplished by treating the relevant uncertain parameters as random variables and deploying two alternative probabilistic models to estimate their effect on predictive uncertainty. A physics‐based model is solved with a stochastic collocation method and is supplemented by a global sensitivity analysis. A second model represents the quantities of interest as polynomials of random inputs and has a virtually negligible computational cost, which enables one to explore any number of risk‐related contamination scenarios. For a typical oil‐spill scenario, our method can be used to identify key flow and transport parameters affecting the risk indices, to elucidate texture‐dependent behavior of different soils, and to evaluate, with a degree of confidence specified by the decision‐maker, the extent of contamination and the correspondent remediation costs.

Evaluation of Unknown Groundwater Contaminant Sources Characterization Efficiency under Hydrogeologic Uncertainty in an Experimental Aquifer Site by Utilizing Surrogate Models

Characterization of unknown groundwater contaminant sources is an important but difficult step in effective groundwater management. The difficulties arise mainly due to the time of contaminant detection which usually happens a long time after the start of contaminant source(s) activities. Usually, limited information is available which also can be erroneous. This study utilizes Self-Organizing Map (SOM) and Gaussian Process Regression (GPR) algorithms to develop surrogate models that can approximate the complex flow and transport processes in a contaminated aquifer. The important feature of these developed surrogate models is that unlike the previous methods, they can be applied independently of any linked optimization model solution for characterizing of unknown groundwater contaminant sources. The performance of the developed surrogate models is evaluated for source characterization in an experimental contaminated aquifer site within the heterogeneous sand aquifer, located at the Botany Basin, New South Wales, Australia. In this study, the measured contaminant concentrations and hydraulic conductivity values are assumed to contain random errors. Simulated responses of the aquifer to randomly specified contamination stresses as simulated by using a three-dimensional numerical simulation model are utilized for initial training of the surrogate models. The performance evaluation results obtained by using different surrogate models are also compared. The evaluation results demonstrate the different capabilities of the developed surrogate models. These capabilities lead to development of an efficient methodology for source characterization based on utilizing the trained and tested surrogate models in an inverse mode. The obtained results are satisfactory and show the potential applicability of the SOM and GPR-based surrogate models for unknown groundwater contaminant source characterization in an inverse mode.

A Quantitative Groundwater Resource Management under Uncertainty Using a Retrospective Optimization Framework

Water resources are a major concern for any socio-economic development. As the quality of many surface fresh water sources increasingly deteriorate, more pressure is being imparted into groundwater aquifers. Since groundwater and the aquifers that host it are inherently vulnerable to anthropogenic impacts, there is a need for sustainable pumping strategies. However, groundwater resource management is challenging due to the heterogeneous nature of aquifer systems. Aquifer hydrogeology is highly uncertain, and thus it is imperative that this uncertainty is accounted for when managing groundwater resource pumping. This, therefore, underscores the need for an efficient optimization tool which can sustainably manage the resource under uncertainty conditions. In this paper, we apply a procedure which is new within the context of groundwater resource management—the Retrospective Optimization Approximation (ROA) method. This method is capable of designing sustainable groundwater pumping strategies for aquifers which are characterized by uncertainty arising due to scarcity of input data. ROA framework solves and evaluates a sequence of optimization sub-problems in an increasing number of realizations. We used k-means clustering sampling technique for the realizations selection. The methodology is demonstrated through application to an hypothetical example. The optimization problem was solved and analyzed using “Active Set” algorithm implemented under MATLAB environment. The results indicate that the ROA sampling based method is a promising approach for optimizing groundwater pumping rates under conditions of hydrogeological uncertainty.

A stochastic optimization framework for the restoration of an over-exploited aquifer

ABSTRACTThis study investigates the impact of hydraulic conductivity uncertainty on the sustainable management of the aquifer of Lake Karla, Greece, using the stochastic optimization approach. The lack of surface water resources in combination with the sharp increase in irrigation needs in the basin over the last 30 years have led to an unprecedented degradation of the aquifer. In addition, the lack of data regarding hydraulic conductivity in a heterogeneous aquifer leads to hydrogeologic uncertainty. This uncertainty has to be taken into consideration when developing the optimization procedure in order to achieve the aquifer’s sustainable management. Multiple Monte Carlo realizations of this spatially-distributed parameter are generated and groundwater flow is simulated for each one of them. The main goal of the sustainable management of the ‘depleted’ aquifer of Lake Karla is two-fold: to determine the optimum volume of renewable groundwater that can be extracted, while, at the same time, restoring its water table to a historic high level. A stochastic optimization problem is therefore formulated, based on the application of the optimization method for each of the aquifer’s multiple stochastic realizations in a future period. In order to carry out this stochastic optimization procedure, a modelling system consisting of a series of interlinked models was developed. The results show that the proposed stochastic optimization framework can be a very useful tool for estimating the impact of hydraulic conductivity uncertainty on the management strategies of a depleted aquifer restoration. They also prove that the optimization process is affected more by hydraulic conductivity uncertainty than the simulation process.Editor Z.W. Kundzewicz; Guest editor S. Weijs

Delineating well-head protection areas under conditions of hydrogeological uncertainty. A case-study application in northern Greece

The delineation of well-head protection areas, especially for water supply wells, is of most importance in ensuring water quality. The areas delineated under this concept must be large enough to ensure that no pollutants will end-up and be pumped-off by abstraction wells but at the same time, small enough to minimize the social and economic cost of applying restriction measures to nearby land owners. The whole concept of applying well-head protection areas is based on the recognition of the hydrogeological parameters of the aquifer. The fact that these parameters are, by nature, characterized by uncertainty makes the whole application very difficult. In this paper, the delineation of well-head protection areas is simulated through a mathematical model and the methodology is applied on the aquifer of Moudania in Chalkidiki, taking into consideration a number of equally probable realizations of the geological structure of the aquifer. This aquifer is characterized by the spatial variability of its hydrogeological parameters, making the introduction of uncertainty in its simulation, an absolute necessity.

The Value of Hydraulic Conductivity Information for the Optimal Restoration of an Over-exploited Aquifer

A stochastic management tool is developed and applied in order to evaluate the worth of hydraulic conductivity data on the optimal restoration and quantitative management of the over-exploited aquifer of Lake Karla watershed in Greece. This tool consists of six models (one geostatistical, four simulation models and one management model) and combines the methodologies of: stochastic simulation-optimization, Bayesian analysis and the value of information analysis. The four simulation models (surface hydrology, reservoir operation, lake-aquifer interaction and hydrogeology) are interlinked in order to satisfy the needs of integrated simulation at the watershed scale. The heterogeneity and the lack of sufficient data of hydraulic conductivity create uncertainty on the hydraulic heads estimation. Monte Carlo realizations of hydraulic conductivity are being performed with the use of geostatistical tools and imported to the groundwater model to give multiple stochastic realizations of the aquifer. A Monte Carlo based optimization problem is then applied for each aquifer realization in order to determine the optimal aquifer's restoration management strategy. Optimal strategy has been defined the one that combines the maximum possible volume of extracted groundwater and the optimal well's position with the least financial cost, under the environmental constraint of restoring the aquifer water table. The hydrogeological uncertainty is being transformed into financial uncertainty through the optimization problem, as certain risks for the decision maker are being introduced. To avoid hydraulic head underestimation, a Bayesian decision analysis for the hydraulic conductivity data collection is being applied on each optimal solution. The worth of the new hydraulic conductivity data can be evaluated by quantifying the reduction of both hydrogeological and financial uncertainties. The results prove that there is a certain number of new hydraulic conductivity measurements up to which the profit by reducing financial uncertainty exceeds the measurement cost.

Book Review: “Effective Parameters of Hydrogeological Models” May Lead Readers Safely Through the Deep Waters of Uncertainty

Hydrogeological models are useful tools for understanding complex interactions in hydrogeological systems. Obtaining quantitative hydrogeological predictions allows one to make data-driven decisions. Models have become a widely used tool to deal with various practical and theoretical problems in hydrogeology; however, the difficulty of measuring aquifer properties and the complexity of hydrogeological systems indicate that data measurements and model predictions are subject to significant uncertainty. The second edition of “Effective Parameters of Hydrogeological Models” by Vikenti Gorokhovski (2014) (Springer Hydrogeology Series, Hardback ISBN 978-3-319-03568-0, eBook ISBN 978-3-319-03569-7) is an excellent introduction to hydrogeological models and the variety of methods to treat uncertainty surrounding models. The book contains 11 chapters, 68 illustrations with 35 in color. This book is part of the Springer Hydrogeology Series, which seeks to publish a broad portfolio of scientific books for researchers, students, and others interested in hydrogeology. The book is an updated version of the first edition (2012), also published by Springer as part of the Springer Briefs in Earth Sciences Series. The second edition contains a new chapter on advective solute transport through porous media, which provides a more comprehensive view of the topic. As its title indicates, the book introduces methods to evaluate model parameters that are critical to effective hydrogeological simulations. It is an excellent addition to the existing literature related to model uncertainty analysis, and will be of great value to hydrogeologists, modelers and hydrogeological students, and potentially useful for decision makers, project managers and practitioners. The second edition advances the field of hydrogeological uncertainty analysis in terms of both theory and methodology. The fact that hydrogeological model predictions are highly uncertain requires critical evaluation of model validity and model performance and this has attracted a great deal of research attention. As Carrera et al. (1993) and Wu and Zeng (2013) have pointed out, three types of uncertainty are involved in hydrogeological modeling: (1) uncertainty associated with Environ. Process. (2014) 1:187–192 DOI 10.1007/s40710-014-0008-8

Allocating risk capital for a brownfields redevelopment project under hydrogeological and financial uncertainty

In this study, we defined risk capital as the contingency fee or insurance premium that a brownfields redeveloper needs to set aside from the sale of each house in case they need to repurchase it at a later date because the indoor air has been detrimentally affected by subsurface contamination. The likelihood that indoor air concentrations will exceed a regulatory level subject to subsurface heterogeneity and source zone location uncertainty is simulated by a physics-based hydrogeological model using Monte Carlo realizations, yielding the probability of failure. The cost of failure is the future value of the house indexed to the stochastic US National Housing index. The risk capital is essentially the probability of failure times the cost of failure with a surcharge to compensate the developer against hydrogeological and financial uncertainty, with the surcharge acting as safety loading reflecting the developers' level of risk aversion. We review five methodologies taken from the actuarial and financial literature to price the risk capital for a highly stylized brownfield redevelopment project, with each method specifically adapted to accommodate our notion of the probability of failure. The objective of this paper is to develop an actuarially consistent approach for combining the hydrogeological and financial uncertainty into a contingency fee that the brownfields developer should reserve (i.e. the risk capital) in order to hedge their risk exposure during the project. Results indicate that the price of the risk capital is much more sensitive to hydrogeological rather than financial uncertainty. We use the Capital Asset Pricing Model to estimate the risk-adjusted discount rate to depreciate all costs to present value for the brownfield redevelopment project. A key outcome of this work is that the presentation of our risk capital valuation methodology is sufficiently generalized for application to a wide variety of engineering projects.

Conceptual hydrogeological model of volcanic Easter Island (Chile) after chemical and isotopic surveys

Most human activities and hydrogeological information on small young volcanic islands are near the coastal area. There are almost no hydrological data from inland areas, where permanent springs and/or boreholes may be rare or nonexistent. A major concern is the excessive salinity of near-the-coast wells. Obtaining a conceptual hydrogeological model is crucial for groundwater resources development and management. Surveys of water seepages and rain for chemical and environmental isotope contents may provide information on the whole island groundwater flow conditions, in spite of remaining geological and hydrogeological uncertainties. New data from Easter Island (Isla de Pascua), in the Pacific Ocean, are considered. Whether Easter Island has a central low permeability volcanic “core” sustaining an elevated water table remains unknown. Average recharge is estimated at 300–400 mm/year, with a low salinity of 15–50 mg/L Cl. There is an apron of highly permeable volcanics that extends to the coast. The salinity of near-the-coast wells, >1,000 mg/L Cl, is marine in origin. This is the result of a thick mixing zone of island groundwater and encroached seawater, locally enhanced by upconings below pumping wells. This conceptual model explains what is observed, in the absence of inland boreholes and springs.

A risk‐driven approach for subsurface site characterization

We present a probabilistic framework to addressing adverse human health effects due to groundwater contamination. One of the main challenges in health risk assessment is in relating it to subsurface data acquisition and to improvement in our understanding of human physiological responses to contamination. In this paper we propose to investigate this problem through an approach that integrates flow, transport and human health risk models with hydrogeological characterization. A human health risk cumulative distribution function is analytically developed to account for both uncertainty and variability in hydrogeological as well as human physiological parameters. With our proposed approach, we investigate under which conditions the reduction of uncertainties from flow physics, human physiology and exposure related parameters might contribute to a better understanding of human health risk assessment. Results indicate that the human health risk cumulative distribution function becomes less sensitive to uncertainty in physiological parameters at lower risk values associated with longer traveltimes. We also present a graphical tool that allows to investigate the relative impact of hydrogeological and physiological parameters in human health risk. A metric α that relates hydrogeological uncertainty to physiological uncertainty was developed to help decision makers set priorities in data acquisition. Other results show that the worth of hydrogeological characterization in human health risk depends on the time the contaminant plume takes to cross the control plane and on the exposure duration of the population to certain chemicals.

Quantifying geological uncertainty for flow and transport modeling in multi-modal heterogeneous formations

In this work, we address the problem of characterizing the heterogeneity and uncertainty of hydraulic properties for complex geological settings. Hereby, we distinguish between two scales of heterogeneity, namely the hydrofacies structure and the intrafacies variability of the hydraulic properties. We employ multiple-point geostatistics to characterize the hydrofacies architecture. The multiple-point statistics are borrowed from a training image that is designed to reflect the prior geological conceptualization. The intrafacies variability of the hydraulic properties is represented using conventional two-point correlation methods, more precisely, spatial covariance models under a multi-Gaussian spatial law. We address the different levels and sources of uncertainty in characterizing the subsurface heterogeneity, and explore their effect on groundwater flow and transport predictions. Typically, uncertainty is assessed by way of many images, termed realizations, of a fixed statistical model. However, in many cases, sampling from a fixed stochastic model does not adequately represent the space of uncertainty. It neglects the uncertainty related to the selection of the stochastic model and the estimation of its input parameters. We acknowledge the uncertainty inherent in the definition of the prior conceptual model of aquifer architecture and in the estimation of global statistics, anisotropy, and correlation scales. Spatial bootstrap is used to assess the uncertainty of the unknown statistical parameters. As an illustrative example, we employ a synthetic field that represents a fluvial setting consisting of an interconnected network of channel sands embedded within finer-grained floodplain material. For this highly non-stationary setting we quantify the groundwater flow and transport model prediction uncertainty for various levels of hydrogeological uncertainty. Results indicate the importance of accurately describing the facies geometry, especially for transport predictions.