Stochastic multimedia risk assessment for a site with contaminated groundwater

 There exist many sites with contaminated groundwater because of inappropriate handling or disposal of hazardous materials or wastes. Health risk assessment is an important tool to evaluate the potential environmental and health impacts of these contaminated sites. It is also becoming an important basis for determining whether risk reduction is needed and what actions should be initiated. However, in research related to groundwater risk assessment and management, consideration of multimedia risk assessment and the separation of the uncertainty due to lack of knowledge and the variability due to natural heterogeneity are rare. This study presents a multimedia risk assessment framework with the integration of multimedia transfer and multi-pathway exposure of groundwater contaminants, and investigates whether multimedia risk assessment and the separation of uncertainty and variability can provide a better basis for risk management decisions. The results of the case study show that a decision based on multimedia risk assessment may differ from one based on risk resulting from groundwater only. In particular, the transfer from groundwater to air imposes a health threat to some degree. By using a methodology that combines Monte Carlo simulation, a rank correlation coefficient, and an explicit decision criterion to identify information important to the decision, the results obtained when uncertainty and variability are separate differ from the ones without such separation. In particular, when higher percentiles of uncertainty and variability distributions are considered, the method separating uncertainty and variability identifies TCE concentration as the single most important input parameter, while the method that does not distinguish the two identifies four input parameters as the important information that would influence a decision on risk reduction.     

Groundwater Availability as Constrained by Hydrogeology and Environmental Flows

Groundwater pumping from aquifers in hydraulic connection with nearby streams has the potential to cause adverse impacts by decreasing flows to levels below those necessary to maintain aquatic ecosystems. The recent passage of the Great Lakes‐St. Lawrence River Basin Water Resources Compact has brought attention to this issue in the Great Lakes region. In particular, the legislation requires the Great Lakes states to enact measures for limiting water withdrawals that can cause adverse ecosystem impacts. This study explores how both hydrogeologic and environmental flow limitations may constrain groundwater availability in the Great Lakes Basin. A methodology for calculating maximum allowable pumping rates is presented. Groundwater availability across the basin may be constrained by a combination of hydrogeologic yield and environmental flow limitations varying over both local and regional scales. The results are sensitive to factors such as pumping time, regional and local hydrogeology, streambed conductance, and streamflow depletion limits. Understanding how these restrictions constrain groundwater usage and which hydrogeologic characteristics and spatial variables have the most influence on potential streamflow depletions has important water resources policy and management implications.     

Groundwater Modeling in Integrated Water Resources Management—Visions for 2020

Groundwater modeling is undergoing a change from traditional stand-alone studies toward being an integrated part of holistic water resources management procedures. This is illustrated by the development in Denmark, where comprehensive national databases for geologic borehole data, groundwater-related geophysical data, geologic models, as well as a national groundwater-surface water model have been established and integrated to support water management. This has enhanced the benefits of using groundwater models. Based on insight gained from this Danish experience, a scientifically realistic scenario for the use of groundwater modeling in 2020 has been developed, in which groundwater models will be a part of sophisticated databases and modeling systems. The databases and numerical models will be seamlessly integrated, and the tasks of monitoring and modeling will be merged. Numerical models for atmospheric, surface water, and groundwater processes will be coupled in one integrated modeling system that can operate at a wide range of spatial scales. Furthermore, the management systems will be constructed with a focus on building credibility of model and data use among all stakeholders and on facilitating a learning process whereby data and models, as well as stakeholders' understanding of the system, are updated to currently available information. The key scientific challenges for achieving this are (1) developing new methodologies for integration of statistical and qualitative uncertainty; (2) mapping geological heterogeneity and developing scaling methodologies; (3) developing coupled model codes; and (4) developing integrated information systems, including quality assurance and uncertainty information that facilitate active stakeholder involvement and learning.     

Assessing hydrological model predictive uncertainty using stochastically generated geological models

In distributed and coupled surface water–groundwater modelling, the uncertainty from the geological structure is unaccounted for if only one deterministic geological model is used. In the present study, the geological structural uncertainty is represented by multiple, stochastically generated geological models, which are used to develop hydrological model ensembles for the Norsminde catchment in Denmark. The geological models have been constructed using two types of field data, airborne geophysical data and borehole well log data. The use of airborne geophysical data in constructing stochastic geological models and followed by the application of such models to assess hydrological simulation uncertainty for both surface water and groundwater have not been previously studied. The results show that the hydrological ensemble based on geophysical data has a lower level of simulation uncertainty, but the ensemble based on borehole data is able to encapsulate more observation points for stream discharge simulation. The groundwater simulations are in general more sensitive to the changes in the geological structure than the stream discharge simulations, and in the deeper groundwater layers, there are larger variations between simulations within an ensemble than in the upper layers. The relationship between hydrological prediction uncertainties measured as the spread within the hydrological ensembles and the spatial aggregation scale of simulation results has been analysed using a representative elementary scale concept. The results show a clear increase of prediction uncertainty as the spatial scale decreases. Copyright © 2015 John Wiley & Sons, Ltd.     

Stochastic environmental risk analysis: an integrated methodology for predicting cancer risk from contaminated groundwater

Stochastic environmental risk assessment considers the effects of numerous biological, chemical, physical, behavioral and physiological processes that involve elements of uncertainty and variability. A methodology for predicting health risks to individuals from contaminated groundwater is presented that incorporates the elements of uncertainty and variability in geological heterogeneity, physiological exposure parameters, and in cancer potency. An idealized groundwater basin is used to perform a parametric sensitivity study to assess the relative impact of (a) geologic uncertainty, (b) behavioral and physiological variability in human exposure and (c) uncertainty in cancer potency on the prediction of increased cancer risk to individuals in a population exposed to contaminants in household water supplied from groundwater. A two-dimensional distribution (or surface) of human health risk was generated as a result of the simulations. Cuts in this surface (fractiles of variability and percentiles of uncertainty) are then used as a measure of relative importance of various model components on total uncertainty and variability. A case study for perchloroethylene or PCE, shows that uncertainty and variability in hydraulic conductivity play an important role in predicting human health risk that is on the same order of influence as uncertainty of cancer potency.     

Calibrating a Salt Water Intrusion Model with Time‐Domain Electromagnetic Data

Salt water intrusion models are commonly used to support groundwater resource management in coastal aquifers. Concentration data used for model calibration are often sparse and limited in spatial extent. With airborne and ground‐based electromagnetic surveys, electrical resistivity models can be obtained to provide high‐resolution three‐dimensional models of subsurface resistivity variations that can be related to geology and salt concentrations on a regional scale. Several previous studies have calibrated salt water intrusion models with geophysical data, but are typically limited to the use of the inverted electrical resistivity models without considering the measured geophysical data directly. This induces a number of errors related to inconsistent scales between the geophysical and hydrologic models and the applied regularization constraints in the geophysical inversion. To overcome these errors, we perform a coupled hydrogeophysical inversion (CHI) in which we use a salt water intrusion model to interpret the geophysical data and guide the geophysical inversion. We refer to this methodology as a Coupled Hydrogeophysical Inversion‐State (CHI‐S), in which simulated salt concentrations are transformed to an electrical resistivity model, after which a geophysical forward response is calculated and compared with the measured geophysical data. This approach was applied for a field site in Santa Cruz County, California, where a time‐domain electromagnetic (TDEM) dataset was collected. For this location, a simple two‐dimensional cross‐sectional salt water intrusion model was developed, for which we estimated five uniform aquifer properties, incorporating the porosity that was also part of the employed petrophysical relationship. In addition, one geophysical parameter was estimated. The six parameters could be resolved well by fitting more than 300 apparent resistivities that were comprised by the TDEM dataset. Except for three sounding locations, all the TDEM data could be fitted close to a root‐mean‐square error of 1. Possible explanations for the poor fit of these soundings are the assumption of spatial uniformity, fixed boundary conditions and the neglecting of 3D effects in the groundwater model and the TDEM forward responses.     

Time‐Elevation Head Sections: Improved Visualization of Data from Multilevels

Hydrogeologic investigations of fractured rock are evolving toward increasing spatial and temporal resolution with increasing use of multilevel systems with 10 or more intervals in a single borehole, each with auto‐sampling sensors monitoring pressure, temperature or chemistry for weeks or months, creating large quantities of densely sampled data (time and space). These data are typically displayed as hydrographs for analysis of site‐specific controls on groundwater flow. We present a method for presentation of high density pressure head data from multilevel installations referred to as time‐elevation head (TEH) sections that improves visualization of spatial and temporal responses of the hydrogeologic system to external stresses. Data collected from two multilevel installations, each with 13 functioning pressure transducers monitoring the upper 40 m of a dolostone aquifer, over a period of 83 d, prior to, during and after a pumping test are used to present TEH sections and examples of data processing. TEH sections are produced using commercially available software designed for geophysical data collected at closely spaced intervals along sub‐parallel lines. These algorithms perform calculations orthogonally either in time (“X” axis) or elevation (“Y” axis) to interpolate a regular grid of head and subsequently when filtering is used to identify subtle trends within the data. The base and filtered TEH sections are used to interpret response of the system to transients and infer hydrogeologic characteristics of the site. The utility of the process is dependent on the precision and accuracy of the head data as well as an informed user to avoid introducing spurious features into the sections.     

A New Assessment Framework for Transience in Hydrogeological Systems

The importance of transience in the management of hydrogeologic systems is often uncertain. We propose a clear framework for determining the likely importance of transient behavior in groundwater systems in a management context. The framework incorporates information about aquifer hydraulics, hydrological drivers, and time scale of management. It is widely recognized that aquifers respond on different timescales to hydrological change and that hydrological drivers themselves, such as climate, are not stationary in time. We propose that in order to assess whether transient behavior is likely to be of practical importance, three factors need to be examined simultaneously: (1) aquifer response time, which can be expressed in terms of the response to a step hydrological change (τ_step) or periodic change (τ_cycle); (2) temporal variation of the dominant hydrological drivers, such as dominant climatic systems in a region; (3) the management timescale and spatial scale of interest. Graphical tools have been developed to examine these factors in conjunction, and assess how important transient behavior is likely to be in response to particular hydrological drivers, and thus which drivers are most likely to induce transience in a specified management timeframe. The method is demonstrated using two case studies; a local system that responds rapidly and is managed on yearly to decadal timeframes and a regional system that exhibits highly delayed responses and was until recently being assessed as a high level nuclear waste repository site. Any practical groundwater resource problem can easily be examined using the proposed framework.     

Induced Temperature Gradients to Examine Groundwater Flowpaths in Open Boreholes

Techniques for characterizing the hydraulic properties and groundwater flow processes of aquifers are essential to design hydrogeologic conceptual models. In this study, rapid time series temperature profiles within open‐groundwater wells in fractured rock were measured using fiber optic distributed temperature sensing (FO‐DTS). To identify zones of active groundwater flow, two continuous electrical heating cables were installed alongside a FO‐DTS cable to heat the column of water within the well and to create a temperature difference between the ambient temperature of the groundwater in the aquifer and that within the well. Additional tests were performed to examine the effects of pumping on hydraulic fracture interconnectivity around the well and to identify zones of increased groundwater flow. High‐ and low‐resolution FO‐DTS cable configurations were examined to test the sensitivities of the technique and compared with downhole video footage and geophysical logging to confirm the zones of active groundwater flow. Two examples are presented to demonstrate the usefulness of this new technique for rapid characterization of fracture zones in open boreholes. The combination of the FO‐DTS and heating cable has excellent scope as a rapid appraisal tool for borehole construction design and improving hydrogeologic conceptual models.     

Constraining complex aquifer geometry with geophysics (2-D ERT and MRS measurements) for stochastic modelling of groundwater flow

Stochastic modelling is a useful way of simulating complex hard-rock aquifers as hydrological properties (permeability, porosity etc.) can be described using random variables with known statistics. However, very few studies have assessed the influence of topological uncertainty (i.e. the variability of thickness of conductive zones in the aquifer), probably because it is not easy to retrieve accurate statistics of the aquifer geometry, especially in hard rock context. In this paper, we assessed the potential of using geophysical surveys to describe the geometry of a hard rock-aquifer in a stochastic modelling framework.The study site was a small experimental watershed in South India, where the aquifer consisted of a clayey to loamy–sandy zone (regolith) underlain by a conductive fissured rock layer (protolith) and the unweathered gneiss (bedrock) at the bottom. The spatial variability of the thickness of the regolith and fissured layers was estimated by electrical resistivity tomography (ERT) profiles, which were performed along a few cross sections in the watershed. For stochastic analysis using Monte Carlo simulation, the generated random layer thickness was made conditional to the available data from the geophysics. In order to simulate steady state flow in the irregular domain with variable geometry, we used an isoparametric finite element method to discretize the flow equation over an unstructured grid with irregular hexahedral elements.The results indicated that the spatial variability of the layer thickness had a significant effect on reducing the simulated effective steady seepage flux and that using the conditional simulations reduced the uncertainty of the simulated seepage flux.As a conclusion, combining information on the aquifer geometry obtained from geophysical surveys with stochastic modelling is a promising methodology to improve the simulation of groundwater flow in complex hard-rock aquifers.     

Characterizing Heterogeneity in Infiltration Rates During Managed Aquifer Recharge

Infiltration rate is the key parameter that describes how water moves from the surface into a groundwater aquifer during managed aquifer recharge (MAR). Characterization of infiltration rate heterogeneity in space and time is valuable information for MAR system operation. In this study, we utilized fiber optic distributed temperature sensing (FO‐DTS) observations and the phase shift of the diurnal temperature signal between two vertically co‐located fiber optic cables to characterize infiltration rate spatially and temporally in a MAR basin. The FO‐DTS measurements revealed spatial heterogeneity of infiltration rate: approximately 78% of the recharge water infiltrated through 50% of the pond bottom on average. We also introduced a metric for quantifying how the infiltration rate in a recharge pond changes over time, which enables FO‐DTS to be used as a method for monitoring MAR and informing maintenance decisions. By monitoring this metric, we found high‐spatial variability in how rapidly infiltration rate changed during the test period. We attributed this variability to biological pore clogging and found a relationship between high initial infiltration rate and the most rapid pore clogging. We found a strong relationship (R² = 0.8) between observed maximum infiltration rates and electrical resistivity measurements from electrical resistivity tomography data taken in the same basin when dry. This result shows that the combined acquisition of DTS and ERT data can improve the design and operation of a MAR pond significantly by providing the critical information needed about spatial variability in parameters controlling infiltration rates.     

A Bayesian trans-dimensional approach for the fusion of multiple geophysical datasets

We propose a Bayesian fusion approach to integrate multiple geophysical datasets with different coverage and sensitivity. The fusion strategy is based on the capability of various geophysical methods to provide enough resolution to identify either subsurface material parameters or subsurface structure, or both. We focus on electrical resistivity as the target material parameter and electrical resistivity tomography (ERT), electromagnetic induction (EMI), and ground penetrating radar (GPR) as the set of geophysical methods. However, extending the approach to different sets of geophysical parameters and methods is straightforward. Different geophysical datasets are entered into a trans-dimensional Markov chain Monte Carlo (McMC) search-based joint inversion algorithm. The trans-dimensional property of the McMC algorithm allows dynamic parameterisation of the model space, which in turn helps to avoid bias of the post-inversion results towards a particular model. Given that we are attempting to develop an approach that has practical potential, we discretize the subsurface into an array of one-dimensional earth-models. Accordingly, the ERT data that are collected by using two-dimensional acquisition geometry are re-casted to a set of equivalent vertical electric soundings. Different data are inverted either individually or jointly to estimate one-dimensional subsurface models at discrete locations. We use Shannon's information measure to quantify the information obtained from the inversion of different combinations of geophysical datasets. Information from multiple methods is brought together via introducing joint likelihood function and/or constraining the prior information. A Bayesian maximum entropy approach is used for spatial fusion of spatially dispersed estimated one-dimensional models and mapping of the target parameter. We illustrate the approach with a synthetic dataset and then apply it to a field dataset. We show that the proposed fusion strategy is successful not only in enhancing the subsurface information but also as a survey design tool to identify the appropriate combination of the geophysical tools and show whether application of an individual method for further investigation of a specific site is beneficial.     

Developing Data-Rich Video of Surface Water–Groundwater Interactions for Public Engagement

Communication of hydrologic data to the public can be improved by connecting data to the places they represent. In our example of data communication, we coupled hydrologic data with simultaneously collected video as both a scientific and public engagement tool. This note presents a method for collecting spatially and temporally dense datasets of water-quality and geophysical data on small streams and lakes, and for displaying the data in a user-friendly format using commercially available software. With this method, multiple instruments are mounted on a canoe and a controlled survey float is carried out to collect data. The data stream is georeferenced and logged using an Arduino microcontroller to provide detailed information about spatial variability. We employed these continuous data-collection methods at small streams and lakes across Wisconsin, USA. Comparison of stream-float sensor data to lab reported data, data collected by alternative sensors, and previously collected data in our study areas indicates that the low-cost temperature, electrical conductivity, pH, and dissolved oxygen sensors performed well. GoPro cameras recorded video throughout the duration of data collection. Our established water-quality and geophysical data collection methods are inexpensive, fast, and reliable, which qualify them as excellent tools for fine-scale spatial understanding of stream and lake habitats' health. Data-rich video connects point measurements of water properties to the appearance of the native environment. This method helps improve our understanding of groundwater and surface water interactions in complex hydrogeologic systems, enhance communication amongst stakeholders, and provide context when monitoring and managing sensitive habitats.     

The impact of subsurface conceptualization on land energy fluxes

There is a significant body of work demonstrating the importance of hydrologic control on land energy feedbacks. Yet, quantitative data on aquifer conductivity can be difficult to assemble. Furthermore, how subsurface uncertainty propagates into land-surface processes is not well understood. This study analyzes the impact of aquifer characterization on land energy fluxes, using a coupled hydrology–land-surface model. Four gridded subsurface conductivity fields are developed for the Upper Klamath basin using two data sources and different levels of imposed heterogeneity. Each model is forced with the same transient, observed meteorology for 3 years prior to the final year presented here. Results are analyzed to quantify the impact of subsurface heterogeneity on groundwater surface water interactions and spatial patterns in hydrologic variables. Analysis shows that heterogeneity does not fundamentally alter the connection between groundwater and land surface processes. However, differences between scenarios impact the extent and location of the critical zone.     

PROBLEMS IN THE USE OF GEOPHYSICAL INFORMATION WHEN INTERPRETING THE DATA OF SPACECRAFT OBSERVATIONS*

Observations made from space have become almost a standard method of geologic investigation. However, interpretation of space photographs is not always unambiguous. Particularly, the nature and spatial location of various objects photographed from space remain obscure. It is especially difficult to investigate territories where basement rocks are overlain by sedimentary cover. From the example of investigations carried out in the central part of the Russian Platform it is shown how separate lineaments and ovoids reflect the peculiar features of geologic, hydrogeologic and geophysical structure of the region. It has been established that—within the sedimentary cover—they correspond usually to zones of increased fissuredness and are associated with low-amplitude tectonic dislocations. The authors concentrate on the elucidation of the transfer of deep geologic information to the surface. The sedimentary cover is regarded as a communication channel which makes it possible to employ the concepts of information theory for assessing its “transparency”. The contrast due to heterogeneities of the sedimentary cover in space photographs and, consequently, the degree of their manifestation in geologic and physical fields, depends on the water content of the rocks composing the section. Along with hydrogeologic characteristics, geo-electric parameters, such as specific electric resistivity, longitudinal conductivity and transverse resistivity, can serve as criteria of the degree of rock moistening. When the location of lineaments does not coincide with the plane axes of geophysical anomalies, an inclination of the fault plane can be supposed. The angle of inclination is characterized by the deviation of the lineament from the axis of the anomaly. The data resulting from the investigation of selected standard structures were used to develop a classification based on the degree of their expression in hydrodynamic, geoelectric, gravitational, and magnetic fields and depending on the spatial position and peculiarities of geologic arrangement. For geophysical investigations of unknown structures detected by space observations, this classification can be used as the basis for solving inverse problems by the methods of mathematical statistics.     

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.     

Flow relevant covariance localization during dynamic data assimilation using EnKF

Multiphase dynamic data integration into high resolution subsurface models is an integral aspect of reservoir and groundwater management strategies and uncertainty assessment. Over the past two decades, advances in computing and the development and implementation of robust algorithms for automatic history matching have considerably reduced the time and effort associated with subsurface characterization and reduced the subjectivity associated with manual model calibration. However, reliable and accurate subsurface characterization continues to be challenging due to the large number of model unknowns to be estimated using a relatively smaller set of measurements. For ensemble-based methods in particular, the difficulties are compounded by the need for a large number of model replicates to estimate sample-based statistical measures, specifically the covariances and cross-covariances that directly impact the spread of information from the measurement locations to the model parameters. Statistical noise resulting from modest ensemble sizes can overwhelm and degrade the model updates leading to geologically inconsistent subsurface models. In this work we propose to address the difficulties in the implementation of the ensemble Kalman filter (EnKF) for operational data integration problems. The methods described here use streamline-derived information to identify regions within the reservoir that will have a maximum impact on the dynamic response. This is achieved through spatial localization of the sample-based cross-covariance estimates between the measurements and the model unknowns using streamline trajectories. We illustrate the approach with a synthetic example and a large field-study that demonstrate the difficulties with the traditional EnKF implementation. In both the numerical experiments, it is shown that these challenges are addressed using flow relevant conditioning of the cross-covariance matrix. By mitigating sampling error in the cross-covariance estimates, the proposed approach provides significant computational savings through the use of modest ensemble sizes, and consequently offers the opportunity for use with large field-scale groundwater and reservoir characterization studies.