Fracture control of ground water flow and water chemistry in a rock aquitard

There are few studies on the hydrogeology of sedimentary rock aquitards although they are important controls in regional ground water flow systems. We formulate and test a three-dimensional (3D) conceptual model of ground water flow and hydrochemistry in a fractured sedimentary rock aquitard to show that flow dynamics within the aquitard are more complex than previously believed. Similar conceptual models, based on regional observations and recently emerging principles of mechanical stratigraphy in heterogeneous sedimentary rocks, have previously been applied only to aquifers, but we show that they are potentially applicable to aquitards. The major elements of this conceptual model, which is based on detailed information from two sites in the Maquoketa Formation in southeastern Wisconsin, include orders of magnitude contrast between hydraulic diffusivity (K/S(s)) of fractured zones and relatively intact aquitard rock matrix, laterally extensive bedding-plane fracture zones extending over distances of over 10 km, very low vertical hydraulic conductivity of thick shale-rich intervals of the aquitard, and a vertical hydraulic head profile controlled by a lateral boundary at the aquitard subcrop, where numerous surface water bodies dominate the shallow aquifer system. Results from a 3D numerical flow model based on this conceptual model are consistent with field observations, which did not fit the typical conceptual model of strictly vertical flow through an aquitard. The 3D flow through an aquitard has implications for predicting ground water flow and for planning and protecting water supplies.     

Certainty, uncertainty, and the spatiality of disease: a West Nile Virus example

The problem is not uncertainty—proposed here as an inevitable condition—but the chimera of certainty asserted by most contemporary researchers. Problems of data definition, collection, and their use are reviewed in terms of spatial epidemiology and health data with examples drawn from several areas of contemporary health research. The argument is that preconceptions limit data modeled in a manner assuming its completeness. The result, as the West Nile Virus example seeks to demonstrate, may obscure other patterns and limit avenues of research.     

Assessment of chemical dispersant effectiveness in a wave tank under regular non-breaking and breaking wave conditions

Current chemical dispersant effectiveness tests for product selection are commonly performed with bench-scale testing apparatus. However, for the assessment of oil dispersant effectiveness under real sea state conditions, test protocols are required to have hydrodynamic conditions closer to the natural environment, including transport and dilution effects. To achieve this goal, Fisheries and Oceans Canada and the US Environmental Protection Agency (EPA) designed and constructed a wave tank system to study chemical dispersant effectiveness under controlled mixing energy conditions (regular non-breaking, spilling breaking, and plunging breaking waves). Quantification of oil dispersant effectiveness was based on observed changes in dispersed oil concentrations and oil-droplet size distribution. The study results quantitatively demonstrated that total dispersed oil concentration and breakup kinetics of oil droplets in the water column were strongly dependent on the presence of chemical dispersants and the influence of breaking waves. These data on the effectiveness of dispersants as a function of sea state will have significant implications in the drafting of future operational guidelines for dispersant use at sea.     

A risk assessment model for produced water discharge from offshore petroleum platforms-development and validation

Produced water discharge accounts for the greater portion of wastes arising from offshore oil and gas production operations. Development and expansion of Canada’s offshore oil and gas reserves has led to concerns over the potential long-term impacts of produced water discharges to the ocean. To examine this emerging environmental issue at a regional scale, an integrated risk assessment approach was developed in this study based on the princeton ocean model (POM), a random walk (RW) and Monte Carlo simulation. The use of water quality standards arrayed in a Monte Carlo design in the developed approach has served to reflect uncertainties and quantify environmental risks associated with produced water discharge. The model was validated against field data from a platform operating off Canada’s east coast, demonstrating its usefulness in supporting effective management of future produced water discharge.     

Toxicities of antifouling biocide Irgarol 1051 and its major degraded product to marine primary producers

Irgarol 1051 (2-methythiol-4-tert-butylamino-6-cyclopropylamino-s-triazine) is an algaecide commonly used in antifouling paints. It undergoes photodegradation which yields M1 (2-methylthio-4-tert-butylamino-6-amino-s-triazine) as its major and most stable degradant. Elevated levels of both Irgarol and M1 have been detected in coastal waters worldwide; however, ecotoxicity effects of M1 to various marine autotrophs such as cyanobacteria are still largely unknown. This study firstly examined and compared the 96 h toxicities of Irgarol and M1 to the cyanobacterium Chroococcus minor and two marine diatom species, Skeletonema costatum and Thalassiosira pseudonana. Our results suggested that Irgarol was consistently more toxic to all of the three species than M1 (96 h EC50 values: C. minor, 7.71 microug L(-1) Irgarol vs. > 200 microg L(-1) M1; S. costatum, 0.29 microg L(-1) Irgarol vs. 11.32 microg L(-1)M1; and T. pseudonana, 0.41 microg L(-1) Irgarol vs. 16.50 microg L(-1)M1). Secondly, we conducted a meta-analysis of currently available data on toxicities of Irgarol and M1 to both freshwater and marine primary producers based on species sensitivity distributions (SSDs). Interestingly, freshwater autotrophs are more sensitive to Irgarol than their marine counterparts. For marine autotrophs, microalgae are generally more sensitive to Irgarol than macroalgae and cyanobacteria. With very limited available data on M1 (i.e. five species), M1 might be less toxic than Irgarol; nonetheless this finding warrants further confirmation with additional data on other autotrophic species.     

Discussion of user-defined parameters for recursive subspace identification: Application to seismic response of building structures

Structural damage assessment under external loading, such as earthquake excitation, is an important issue in structural safety evaluation. In this regard, an appropriate data analysis and system identification technique is required to interpret the measured data and to identify the state of the structure. Generally, the recursive system identification algorithm is used. In this study, the recursive subspace identification (RSI) algorithm based on the matrix inversion lemma algorithm with oblique projection technique (RSI-Inversion-Oblique) is applied to investigate the time-varying dynamic characteristics. The user-defined parameters used in the RSI-Inversion-Oblique technique are carefully discussed, which include the size of the data Hankel matrix (i), model order to extract the physical modes, and forgetting factor (FF) to detect the time-varying system modal frequencies. Response data from the Northridge earthquake from the Sherman Oaks building (CSMIP) is used as an example to examine a systematic method to determine the suitable user-defined parameters in RSI. It is concluded that the number of rows in the data Hankel matrix significantly influences the identification of the time-varying fundamental modal frequency of the structure. An algorithmic model order selection method using the eigenvalue distribution of RSI-Inversion can detect the system modal frequencies at each appending data window without causing any abnormality.     

Bayesian approach to contaminant source characterization in water distribution systems: adaptive sampling framework

Bayesian analysis can yield a probabilistic contaminant source characterization conditioned on available sensor data and accounting for system stochastic processes. This paper is based on a previously proposed Markov chain Monte Carlo (MCMC) approach tailored for water distribution systems and incorporating stochastic water demands. The observations can include those from fixed sensors and, the focus of this paper, mobile sensors. Decision makers, such as utility managers, need not wait until new observations are available from an existing sparse network of fixed sensors. This paper addresses a key research question: where is the best location in the network to gather additional measurements so as to maximize the reduction in the source uncertainty? Although this has been done in groundwater management, it has not been well addressed in water distribution networks. In this study, an adaptive framework is proposed to guide the strategic placement of mobile sensors to complement the fixed sensor network. MCMC is the core component of the proposed adaptive framework, while several other pieces are indispensable: Bayesian preposterior analysis, value of information criterion and the search strategy for identifying an optimal location. Such a framework is demonstrated with an illustrative example, where four candidate sampling locations in the small water distribution network are investigated. Use of different value-of-information criteria reveals that while each may lead to different outcomes, they share some common characteristics. The results demonstrate the potential of Bayesian analysis and the MCMC method for contaminant event management.     

An Analysis Platform for Multiscale Hydrogeologic Modeling with Emphasis on Hybrid Multiscale Methods

One of the most significant challenges faced by hydrogeologic modelers is the disparity between the spatial and temporal scales at which fundamental flow, transport, and reaction processes can best be understood and quantified (e.g., microscopic to pore scales and seconds to days) and at which practical model predictions are needed (e.g., plume to aquifer scales and years to centuries). While the multiscale nature of hydrogeologic problems is widely recognized, technological limitations in computation and characterization restrict most practical modeling efforts to fairly coarse representations of heterogeneous properties and processes. For some modern problems, the necessary level of simplification is such that model parameters may lose physical meaning and model predictive ability is questionable for any conditions other than those to which the model was calibrated. Recently, there has been broad interest across a wide range of scientific and engineering disciplines in simulation approaches that more rigorously account for the multiscale nature of systems of interest. In this article, we review a number of such approaches and propose a classification scheme for defining different types of multiscale simulation methods and those classes of problems to which they are most applicable. Our classification scheme is presented in terms of a flowchart (Multiscale Analysis Platform), and defines several different motifs of multiscale simulation. Within each motif, the member methods are reviewed and example applications are discussed. We focus attention on hybrid multiscale methods, in which two or more models with different physics described at fundamentally different scales are directly coupled within a single simulation. Very recently these methods have begun to be applied to groundwater flow and transport simulations, and we discuss these applications in the context of our classification scheme. As computational and characterization capabilities continue to improve, we envision that hybrid multiscale modeling will become more common and also a viable alternative to conventional single‐scale models in the near future.     

Recent shifts in coastline change and shoreline stabilization linked to storm climate change

Since cuspate coastlines are especially sensitive to changes in wave climate, they serve as potential indicators of initial responses to changing wave conditions. Previous work demonstrates that Cape Hatteras and Cape Lookout, North Carolina, which are largely unaffected by shoreline stabilization efforts, have become increasingly asymmetric over the past 30 years, consistent with model predictions for coastline response to increases in Atlantic Ocean summer wave heights and resulting changes in the distribution of wave‐approach angles. Historic and recent shoreline change observations for Cape Fear, North Carolina, and model simulations of coastline response to an increasingly asymmetric wave climate in the presence of beach nourishment, produce comparable differences in shoreline change rates in response to changes in wave climate. Results suggest that the effect of beach nourishment is to compensate for – and therefore to mask – natural responses to wave climate change that might otherwise be discernible in patterns of shoreline change alone. Therefore, this case study suggests that the effects of wave climate change on human‐modified coastlines may be detectable in the spatial and temporal patterns of shoreline stabilization activities. Similar analyses of cuspate features in areas where the change in wave climate is less pronounced (i.e. Fishing Point, Maryland/Virginia) and where local geology appears to exert control on coastline shape (i.e. Cape Canaveral, Florida), suggest that changes in shoreline configuration that may be arising from shifting wave climate are currently limited to sandy wave‐dominated coastlines where the change in wave climate has been most pronounced. However, if hurricane‐generated wave heights continue to increase, large‐scale shifts in patterns of erosion and accretion will likely extend beyond sensitive cuspate features as the larger‐scale coastline shape comes into equilibrium with changing wave conditions. Copyright © 2014 John Wiley & Sons, Ltd.     

Divergence of solutions to perturbation-based advection-dispersion moment equations

Moment equation methods are popular and powerful tools for modeling transport processes in randomly heterogeneous porous media, but the application of these methods to advection-dispersion equations often leads to erroneous oscillations. Perturbative methods, required to close systems of moment equations, become inaccurate for large perturbations; however, little quantitative theory exists for determining when this occurs for advection-dispersion equations. We consider three different methods (asymptotic approximation, Eulerian truncation, and iterative solution) for closing and solving advection-dispersion moment equations describing transport in stratified porous media with random permeability. We obtain approximate analytical expressions for time above which the asymptotic approximation to the mean diverges, in particular quantifying the impact that dispersion has on delaying—but not eliminating—divergence. We demonstrate that Eulerian truncation and iterative solution methods do not eliminate divergent behavior either. Our divergence criteria provide a priori estimates that signal a warning to the practitioner of stochastic advection-dispersion equations to carefully consider whether to apply perturbative approaches.