Geostatistical interpolation of chemical concentration

Measurements of contaminant concentration at a hazardous waste site typically vary over many orders of magnitude and have highly skewed distributions. This work presents a practical methodology for the estimation of solute concentration contour maps and volume averages (needed for mass calculations) from data obtained from the analysis of water and soil samples. The methodology, which is an extension of linear geostatistics, produces a point estimate, i.e., a representative value, as well as a confidence interval, which contains the true value with a given probability. The approach uses a parsimonious model that accounts for the skewness by adding only one parameter to those used in linear geostatistics (variograms or generalized covariances). The resulting nonlinear kriging method is not substantially more difficult to use than linear geostatistics. The methodology is most appropriate when concentration measurements are available on a reasonably dense grid and no additional information (based on modeling flow and transport) can be used. We present and illustrate through an application, a practical approach to estimate all the parameters needed and to select and test the model.     

Global Optimal Design of Ground Water Monitoring Network Using Embedded Kriging

We present a methodology for global optimal design of ground water quality monitoring networks using a linear mixed-integer formulation. The proposed methodology incorporates ordinary kriging (OK) within the decision model formulation for spatial estimation of contaminant concentration values. Different monitoring network design models incorporating concentration estimation error, variance estimation error, mass estimation error, error in locating plume centroid, and spatial coverage of the designed network are developed. A big-M technique is used for reformulating the monitoring network design model to a linear decision model while incorporating different objectives and OK equations. Global optimality of the solutions obtained for the monitoring network design can be ensured due to the linear mixed-integer programming formulations proposed. Performances of the proposed models are evaluated for both field and hypothetical illustrative systems. Evaluation results indicate that the proposed methodology performs satisfactorily. These performance evaluation results demonstrate the potential applicability of the proposed methodology for optimal ground water contaminant monitoring network design.     

A fuzzy approach to reliability based design of storm water drain network

This paper proposes an approach to estimate reliability of a storm water drain (SWD) network in fuzzy framework. It involves: (i) use of proposed fuzzy Monte-Carlo simulation (FMCS) methodology to estimate fuzzy reliability of conduits in the network, (ii) construction of a reliability block diagram (RBD) for the network (system) using suggested guidelines, and (iii) use of the RBD and reliability estimates of the conduits in the network to compute system reliability based on a proposed procedure. In addition, a system reliability based methodology is proposed for design/retrofitting of SWD network by optimization of its conduit dimensions. Conventionally used reliability analysis approaches assume that the cumulative distribution function (CDF) of performance function (marginal safety) of conduits follows Gaussian distribution, which cannot be ensured in the real world scenario. The proposed approach alleviates the need for making such assumptions and can account for linguistic ambiguity in variables defining the performance function. Effectiveness of the proposed approach is demonstrated on a hypothetical SWD network and a real network in Bangalore, India. Comparison of the results obtained from the proposed approach with those from conventional Monte-Carlo simulation (MCS) reliability assessment approach indicated that the estimate of system reliability and conduit reliability are higher with FMCS approach. Consequently, conduit dimensions required to attain required system (network) reliability could be expected to be lower when FMCS approach is used for designing or retrofitting a system.     

Casing Leakage in Monitoring Wells: Detection, Confirmation, and Prevention

A small but significant proportion of all existing monitoring wells may be affected by leakage through the casing, usually at joints. Casing leakage can render data obtained from a monitoring well unreliable. Anomalous water level, water quality, or isotope data from a particular well are an indication of possible leakage. The occurrence of a casing leak can be confirmed by means of a pressure test using water. The magnitude of the leakage flow can be estimated from the pressure test or from the observed head anomaly. Casing leaks can be largely prevented with adequate care during monitoring well installation, but the possibility that data may be affected by casing leaks should always be taken into account during hydrogeological investigations.     

State estimation of tidal hydrodynamics using ensemble Kalman filter

This paper presents a coupling of an ensemble Kalman filter (EnKF) with a discontinuous Galerkin-based, two-dimensional circulation model (DG ADCIRC-2DDI) to improve the state estimation of tidal hydrodynamics including water surface elevations and depth-integrated velocities. The methodology in this paper using EnKF perturbs the modeled hydrodynamics and bottom friction parameterization in the model while assimilating data with inherent error, and demonstrates a capability to apply EnKF within DG ADCIRC-2DDI for data assimilation. Parallel code development presents a unique aspect of the approach taken and is briefly described in the paper, followed by an application to a real estuarine system, the lower St. Johns River in north Florida, for the state estimation of tidal hydrodynamics. To test the value of gauge observations for improving state estimation, a tide modeling case study is performed for the lower St. Johns River successively using one of the four available tide gauging stations in model-data comparison. The results are improved simulations of water surface elevations and depth-integrated velocities using DG ADCIRC-2DDI with EnKF, both locally where data are available and non-locally where data are not available. The methodology, in general, is extensible to other modeling and data applications, for example, the use of remote sensing data, and specifically, can be readily applied as is to study other tidal systems.     

Optimal design of snow stake networks to estimate snow depth in an alpine mountain range

Monitoring and estimation of snow depth in alpine catchments is needed for a proper assessment of management alternatives for water supply in these water resources systems. The distribution of snowpack thickness is usually approached by using field data that come from snow samples collected at a given number of locations that constitute the monitoring network. Optimal design of this network is required to obtain the best possible estimates. Assuming that there is an existing monitoring network, its optimization may imply the selection of an optimal network as a subset of the existing one (if there are no funds to maintain them) or enlarging the existing network by one or more stations (optimal augmentation problem). We propose an optimization procedure that minimizes the total variance in the estimate of snowpack thickness. The novelty of this work is to treat, for the first time, the problem of snow observation network optimization for an entire mountain range rather than for small catchments as done in the previous studies. Taking into account the reduced data available, which is a common problem in many mountain ranges, the importance of a proper design of these observation networks is even larger. Snowpack thickness is estimated by combining regression models to approach the effect of the explanatory variables and kriging techniques to consider the influence of the stakes location. We solve the optimization problems under different hypotheses, studying the impacts of augmentation and reduction, both, one by one and in pairs. We also analyse the sensitivity of results to nonsnow measurements deduced from satellite information. Finally, we design a new optimal network by combining the reduction and augmentation methods. The methodology has been applied to the Sierra Nevada mountain range (southern Spain), where very limited resources are employed to monitor snowfall and where an optimal snow network design could prove critical. An optimal snow observation network is defined by relocating some observation points. It would reduce the estimation variance by around 600 cm² (15%).     

Seismic reliability assessment of urban water networks

We present a framework for the seismic risk assessment of water supply networks, operating in either normal or abnormal conditions. We propose a methodology for assessing the reliability of water pipe networks combining data of past non‐seismic damage and the vulnerability of the network components against seismic loading. Historical data are obtained using records of damages that occur on a daily basis throughout the network and are processed to produce‘survival curves’, depicting their estimated survival rate over time. The fragility of the network components is assessed using the approach suggested in the American Lifelines Alliance guidelines. The network reliability is assessed using graph theory, whereas the system network reliability is calculated using Monte Carlo simulation. The methodology proposed is demonstrated both on a simple, small‐scale, network and also on a real‐scale district metered area from the water network of the city of Limassol, Cyprus. The proposed approach allows the estimation of the probability that the network fails to provide the desired level of service and allows the prioritization of retrofit interventions and of capacity‐upgrade actions pertaining to existing water pipe networks. Copyright © 2013 John Wiley & Sons, Ltd.     

Early detection of brine and CO2 leakage through abandoned wells using pressure and surface-deformation monitoring data: Concept and demonstration

In this paper, we develop a methodology for early detection of potential CO₂ leakage from geological storage formations using pressure and surface-deformation anomalies. The basic idea is based on the fact that leakage-induced pressure signals travel much faster than the migrating CO₂; thus such anomalies may be detected early enough for risk management measures taking effect in avoiding substantial CO₂ leaks. The early detection methodology involves automatic inversion of anomalous brine leakage signals with efficient forward pressure and surface-deformation modeling tools to estimate the location and permeability of leaky features in the caprock. We conduct a global sensitivity analysis to better understand under which conditions pressure anomalies can be clearly identified as leakage signals, and evaluate signal detectability for a broad parameter range considering different detection limits and levels of data noise. The inverse methodology is then applied to two synthetic examples of idealized two-aquifer-and-one aquitard storage systems, with an injection well and a leaky well, for different monitoring scenarios. In Example 1, only pressure data at the monitoring and injection wells are used for leakage detection. Our results show that the accuracy of leakage detection greatly depends on the level of pressure data noise. In Example 2, joint inversion of pressure and surface-deformation measurements significantly improves the speed of convergence toward the true solution of the leakage parameters and enables early leakage detection. In both examples, successful detection is achieved when two monitoring wells are appropriately placed within up to 4 km from the leaky well.     

Estimation of soil hydraulic properties and their uncertainty through the Beerkan infiltration experiment

The Beerkan method based on in situ single‐ring water infiltration experiments along with the relevant specific Beerkan estimation of soil transfer parameters (BEST) algorithm is attractive for simple soil hydraulic characterization. However, the BEST algorithm may lead to erroneous or null values for the saturated hydraulic conductivity and sorptivity especially when there are only few infiltration data points under the transient flow state, either for sandy soil or soils in wet conditions. This study developed an alternative algorithm for analysis of the Beerkan infiltration experiment referred to as BEST‐generalized likelihood uncertainty estimation (GLUE). The proposed method estimates the scale parameters of van Genuchten water retention and Brooks–Corey hydraulic conductivity functions through the GLUE methodology. The GLUE method is a Bayesian Monte Carlo parameter estimation technique that makes use of a likelihood function to measure the goodness‐of‐fit between modelled and observed data. The results showed that using a combination of three different likelihood measurements based on observed transient flow, steady‐state flow and experimental steady‐state infiltration rate made the BEST‐GLUE procedure capable of performing an efficient inverse analysis of Beerkan infiltration experiments. Therefore, it is more applicable for a wider range of soils with contrasting texture, structure, and initial and saturated water content. Copyright © 2015 John Wiley & Sons, Ltd.     

Design and Optimization of a Ground Water Monitoring System Using GIS and Multicriteria Decision Analysis

A GIS-based methodology has been developed to design a ground water monitoring system and implemented for a selected area in Mae-Klong River Basin, Thailand. A multicriteria decision-making analysis has been performed to optimize the network system based on major criteria which govern the monitoring network design such as minimization of cost of construction, reduction of kriging standard deviations, etc. The methodology developed in this study is a new approach to designing monitoring networks which can be used for any site considering site-specific aspects. It makes it possible to choose the best monitoring network from various alternatives based on the prioritization of decision factors.     

Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter

Catchment scale hydrological models are critical decision support tools for water resources management and environment remediation. However, the reliability of hydrological models is inevitably affected by limited measurements and imperfect models. Data assimilation techniques combine complementary information from measurements and models to enhance the model reliability and reduce predictive uncertainties. As a sequential data assimilation technique, the ensemble Kalman filter (EnKF) has been extensively studied in the earth sciences for assimilating in-situ measurements and remote sensing data. Although the EnKF has been demonstrated in land surface data assimilations, there are no systematic studies to investigate its performance in distributed modeling with high dimensional states and parameters. In this paper, we present an assessment on the EnKF with state augmentation for combined state-parameter estimation on the basis of a physical-based hydrological model, Soil and Water Assessment Tool (SWAT). Through synthetic simulation experiments, the capability of the EnKF is demonstrated by assimilating the runoff and other measurements, and its sensitivities are analyzed with respect to the error specification, the initial realization and the ensemble size. It is found that the EnKF provides an efficient approach for obtaining a set of acceptable model parameters and satisfactory runoff, soil water content and evapotranspiration estimations. The EnKF performance could be improved after augmenting with other complementary data, such as soil water content and evapotranspiration from remote sensing retrieval. Sensitivity studies demonstrate the importance of consistent error specification and the potential with small ensemble size in the data assimilation system.     

Direct estimation of seismic response in reduced-degree-of-freedom isolation and energy dissipation systems

A methodology for the development of design tools for direct estimation of peak inelastic response in reduced-degree-of-freedom (RDOF) isolation and energy dissipation systems is presented. The suggested procedure is an extension of an earlier method addressing purely hysteretic isolation systems. Herein, the dynamic equation of motion is first normalised to reduce the number of design parameters that significantly affect the response. The sensitivity of normalised response quantities to the amplitude of the ground motion is then investigated through extensive parametric nonlinear dynamic analyses of isolated single-degree-of-freedom (SDOF) systems with linear viscous damping using code-based target spectra. Regression analysis is subsequently employed to develop generalised design equations (GDEs) suitable for design. Further investigations are made to address nonlinear viscous damping and the effect of the transverse component of seismic action in two-degree-of freedom (2DOF) systems under bidirectional excitation, making the procedure applicable to common bridge isolation schemes. GDEs constitute an alternative to equivalent linearisation approaches commonly adopted by codes, informing the selection among alternative isolation and energy dissipations schemes without requiring iterative analysis. The approach is incorporated in the Deformation-Based Design methodology for seismically isolated bridges in a forthcoming paper.     

A method for non-parametric damage detection through the use of neural networks

A neural network-based approach is presented for the detection of changes in the characteristics of structure-unknown systems. The approach relies on the use of vibration measurements from a ‘healthy’ system to train a neural network for identification purposes. Subsequently, the trained network is fed comparable vibration measurements from the same structure under different episodes of response in order to monitor the health of the structure. The methodology is applied to actual data obtained from ambient vibration measurements on a steel building structure that was damaged under strong seismic motion during the Hyogo-Ken Nanbu Earthquake of 17 January 1995. The measurements were done before and after repairs to the damaged frame were made. A neural network is trained with data after the repairs, which represents ‘healthy’ condition of the building. The trained network, which is subsequently fed data before the repairs, successfully identified the difference between the damaged storey and the undamaged storey. Through this study, it is shown that the proposed approach has the potential of being a practical tool for a damage detection methodology applied to smart civil structures. © 1998 John Wiley & Sons, Ltd.     

Dimensionality reduction for efficient Bayesian estimation of groundwater flow in strongly heterogeneous aquifers

We focus on the Bayesian estimation of strongly heterogeneous transmissivity fields conditional on data sampled at a set of locations in an aquifer. Log-transmissivity, Y, is modeled as a stochastic Gaussian process, parameterized through a truncated Karhunen–Loève (KL) expansion. We consider Y fields characterized by a short correlation scale as compared to the size of the observed domain. These systems are associated with a KL decomposition which still requires a high number of parameters, thus hampering the efficiency of the Bayesian estimation of the underlying stochastic field. The distinctive aim of this work is to present an efficient approach for the stochastic inverse modeling of fully saturated groundwater flow in these types of strongly heterogeneous domains. The methodology is grounded on the construction of an optimal sparse KL decomposition which is achieved by retaining only a limited set of modes in the expansion. Mode selection is driven by model selection criteria and is conditional on available data of hydraulic heads and (optionally) Y. Bayesian inversion of the optimal sparse KLE is then inferred using Markov Chain Monte Carlo (MCMC) samplers. As a test bed, we illustrate our approach by way of a suite of computational examples where noisy head and Y values are sampled from a given randomly generated system. Our findings suggest that the proposed methodology yields a globally satisfactory inversion of the stochastic head and Y fields. Comparison of reference values against the corresponding MCMC predictive distributions suggests that observed values are well reproduced in a probabilistic sense. In a few cases, reference values at some unsampled locations (typically far from measurements) are not captured by the posterior probability distributions. In these cases, the quality of the estimation could be improved, e.g., by increasing the number of measurements and/or the threshold for the selection of KL modes.     

Airborne Thermal Remote Sensing for Estimation of Groundwater Discharge to a River

Traditional methods for studying surface water and groundwater interactions have usually been limited to point measurements, such as geochemical sampling and seepage measurement. A new methodology is presented for quantifying groundwater discharge to a river, by using river surface temperature data obtained from airborne thermal infrared remote sensing technology. The Hot Spot Analysis toolkit in ArcGIS was used to calculate the percentage of groundwater discharge to a river relative to the total flow of the river. This methodology was evaluated in the midstream of the Heihe River in the arid and semiarid northwest China. The results show that the percentage of groundwater discharge relative to the total streamflow was as high as 28%, which is in good agreement with the results from previous geochemical studies. The data analysis methodology used in this study is based on the assumption that the river water is fully mixed except in the areas of extremely low flow velocity, which could lead to underestimation of the amount of groundwater discharge. Despite this limitation, this remote sensing‐based approach provides an efficient means of quantifying the surface water and groundwater interactions on a regional scale.     

Spatial rainfall model using a pattern classifier for estimating missing daily rainfall data

Missing data in daily rainfall records are very common in water engineering practice. However, they must be replaced by proper estimates to be reliably used in hydrologic models. Presented herein is an effort to develop a new spatial daily rainfall model that is specifically intended to fill in gaps in a daily rainfall dataset. The proposed model is different from a convectional daily rainfall generation scheme in that it takes advantage of concurrent measurements at the nearby sites to increase the accuracy of estimation. The model is based on a two-step approach to handle the occurrence and the amount of daily rainfalls separately. This study tested four neural network classifiers for a rainfall occurrence processor, and two regression techniques for a rainfall amount processor. The test results revealed that a probabilistic neural network approach is preferred for determining the occurrence of daily rainfalls, and a stepwise regression with a log-transformation is recommended for estimating daily rainfall amounts.     

Estimation of mean water transit time on a steep hillslope in South Korea using soil moisture measurements and deuterium excess

A method for estimating daily mean transit time (DMTT) within a soil layer was proposed using field measurements of soil moisture. Vertical profiles of soil moisture time series were used for storage estimation. Water fluxes were evaluated through matrix and bypass flow. Variations in soil moisture and soil thickness were used to evaluate matrix flow. Exponential decay in depth of macropores was also used for bypass flow approximation. DMTT evaluation was compared to results obtained from a stable water isotope model using two years of data acquired on a steep granite hillslope in the Sulmachun watershed, South Korea. Various uncertainties in transit time evaluation such as model structure, non‐stationary assumption and data acquisition of existing approaches can be accounted for in the proposed methodology, and the flowpath contribution can be further configured in conjunction with hydrometric measurements. Probability density functions of isotope analyses were partially explained by transit time distributions that were based on soil moisture measurements. Supplementary sensitivity analyses for uncertainty configurations indicate that matrix flow is the primary process in determining transit time distribution while the impact of bypass flow is minor. The feasibility of a DMTT approach over isotope‐based methodologies highlights not only the strength of this proposed method, both in cost and time, but also its further application potential for existing soil moisture measurements. Copyright © 2013 John Wiley & Sons, Ltd.     

Development of a Standard, Pure-Compound Base Gasoline Mixture for Use as a Reference in Field and Laboratory Experiments

In recent years, leaking underground petroleum storage tanks have become a major environmental concern. Federal, state, and local agencies are making efforts to assess, control, and remediate petroleum product leaks. Because petroleum products are mixtures of many compounds and the composition frequently changes, there exists a need for a standard reference mixture that can be used as a basis for comparison in the study of fluid transport properties in porous media and to evaluate leak detection devices. The proposed standard presented here retains the most important liquid and vapor properties (such as vapor density, air diffusion coefficient, and basic chemical constituency) of gasoline mixtures. It also reduces the complexity of the mixture to make it an acceptable standard for routine use in laboratory and field experiments.     

Optimal selection of number and location of rainfall gauges for areal rainfall estimation using geostatistics and simulated annealing

This paper presents a method for establishing an optimal network design for the estimation of areal averages of rainfall events. The problem consists of minimising an objective function which includes both the accuracy of the areal mean estimation (as expressed by the kriging variance of estimation) and the economic cost of the data collection. The well known geostatistical variance-reduction method is used in combination with simulated annealing as an algorithm of minimisation. This methodology has several advantages which will be demonstrated in this paper. Several synthetic examples are shown in order to illustrate the performance of the methodology in two different optimisation problems: the optimal selection of a subset from a set of stations that already exist and the optimal augmentation of a previously existing network.