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• First to use superparamagnetic DNA tagged colloids (SiDNAmag) for hydraulic parameter estimation of a 3D sand aquifer, using monte carlo modelling approach at different ionic strengths. • Hydraulic parameter distributions estimated from sidnamag breakthrough curves through Monte Carlo simulation approach were statistically similar to salt tracer. • SiDNAmags are good candidate for determining hydraulic parameters of saturated 3D sand aquifer at short travel distance (50 cm). We investigated the applicability of Silica encapsulated, superparamagnetic DNA particles (SiDNAmag) in determining aquifer hydraulic parameters at different ionic strengths (1 mM, 5 mM, and 20 mM phosphate buffer) of injection suspension. Thereto, in a homogeneous, unconsolidated sand tank we pulse - injected two uniquely sequenced SiDNAmag at two injection points. At 0.5 m and 0.8 m downstream from the injection points, we measured the concentration of SiDNAmags at three vertically distributed and two horizontally distributed sampling locations. We estimated the hydraulic parameter distributions from the SiDNAmag breakthrough curves through a Monte – Carlo approach and compared the parameter distributions with salt tracer breakthrough curves. Our results indicated that at all the ionic strengths, the times of peak concentrations, and the shapes of the breakthrough curves were similar to the salt tracer. As compared to the salt, a 1 – 3 log units reduction in the maximum effluent concentration of SiDNAmag was due to kinetic attachment. The attachment rate reduced from 1 mM to 5 mM phosphate buffer possibly due to competitive adsorption of phosphate onto the favourable attachment sites. SiDNAmag attachment rate further increased in 20 mM buffer suspension, possibly due to the compression of electric double layer and reduction in energy barrier for attachment. The parameter distributions of hydraulic conductivity (k), effective porosity (n e), longitudinal dispersivity (α L), vertical transverse dispersivity (α TV /α L) and horizontal transverse dispersivity (α TH /α L) estimated from the SiDNAmag and the salt tracer breakthrough curves were statistically similar. Our work contributes to the applicability of colloidal SiDNAmags for determining hydraulic parameters at different ionic strength conditions. [ABSTRACT FROM AUTHOR]

Highlights • Impact of positive porosity-conductivity correlation on probabilistic risk analysis. • Relevance of including porosity variability depends on the metric of interest, the control plane-source distance and the level of conductivity heterogeneity. • A heterogeneous porosity field leads to a reduction of the increased lifetime cancer risk uncertainty. Abstract Heterogeneities in natural porous formations, mainly manifested through the hydraulic conductivity (K) and, to a lesser degree, the porosity (ϕ), largely control subsurface flow and solute transport. The influence of the heterogeneous structure of K on transport processes has been widely studied, whereas less attention is dedicated to the joint heterogeneity of conductivity and porosity fields. Our study employs Monte Carlo simulations to investigate the coupled effect of K − ϕ spatial variability on the transport behavior (and uncertainty) of conservative and reactive plumes within a 3D aquifer domain. We explore multiple scenarios, characterized by different levels of heterogeneity of the geological properties, and compare the computational results from the joint K − ϕ heterogeneous system with the results originating from generally adopted constant ϕ conditions. In our study, the spatially variable K − ϕ fields are positively correlated. We statistically analyze key Environmental Performance Metrics: first arrival times and peak mass fluxes for non-reactive species and increased lifetime cancer risk for reactive chlorinated solvents. The conservative transport simulations show that considering coupled K − ϕ fields decreases the plume dispersion, increases both the first arrival times of solutes and the peak mass fluxes at the observation planes. A positive correlation between aquifer connectivity and peak mass fluxes is identified for both homogeneous and heterogeneous ϕ. Our conservative transport results indicate that the relevance of ϕ variability can depend on the metric of interest, the control plane-source distance as well as the level of heterogeneity of the conductivity field. The analysis on reactive transport shows that ϕ variability only slightly affects the mean increased lifetime cancer risk at the control planes but leads to a considerable reduction of the cancer risk uncertainty. We also see that the sensitivity of cancer risk towards ϕ heterogeneity can be influenced by the level of variability of the conductivity field, the source-to-control plane distance, but is not affected by the manner in which the contaminant concentration is computed. [ABSTRACT FROM AUTHOR]

Hydrologic models are commonly over-smoothed relative to reality, owing to computational limitations and to the difficulty of obtaining accurate high-resolution information. When used in an inversion context, such models may introduce systematic biases which cannot be encapsulated by an unbiased “observation noise” term of the type assumed by standard regularization theory and typical Bayesian formulations. Despite its importance, model error is difficult to encapsulate systematically and is often neglected. Here, model error is considered for an important class of inverse problems that includes interpretation of hydraulic transients and contaminant source history inference: reconstruction of a time series that has been convolved against a transfer function (i.e., impulse response) that is only approximately known. Using established harmonic theory along with two results established here regarding triangular Toeplitz matrices, upper and lower error bounds are derived for the effect of model error on time series recovery for both well-determined and over-determined inverse problems. It is seen that use of additional measurement locations does not improve expected performance in the face of model error. A Monte Carlo study of a realistic hydraulic reconstruction problem is presented, and the lower error bound is seen informative about expected behavior. A possible diagnostic criterion for blind transfer function characterization is also uncovered. [ABSTRACT FROM AUTHOR]

Estimating spatial distributions of the hydraulic conductivity in heterogeneous aquifers has always been an important and challenging task in hydrology. Generally, the hydraulic conductivity field is determined from hydraulic head or pressure measurements. In the present study, we propose to use temperature data as source of information for characterizing the spatial distributions of the hydraulic conductivity field. In this way, we performed a laboratory sandbox experiment with the aim of imaging the heterogeneities of the hydraulic conductivity field from thermal monitoring. During the laboratory experiment, we injected a hot water pulse, which induces a heat plume motion into the sandbox. The induced plume was followed by a set of thermocouples placed in the sandbox. After the temperature data acquisition, we performed a hydraulic tomography using the stochastic Hybrid Monte Carlo approach, also called the Hamiltonian Monte Carlo (HMC) algorithm to invert the temperature data. This algorithm is based on a combination of the Metropolis Monte Carlo method and the Hamiltonian dynamics approach. The parameterization of the inverse problem was done with the Karhunen-Loève (KL) expansion to reduce the dimensionality of the unknown parameters. Our approach has provided successful reconstruction of the hydraulic conductivity field with low computational effort. [ABSTRACT FROM AUTHOR]

Abstract: This paper concerns efficient uncertainty quantification techniques in inverse problems for Richards’ equation which use coarse-scale simulation models. We consider the problem of determining saturated hydraulic conductivity fields conditioned to some integrated response. We use a stochastic parameterization of the saturated hydraulic conductivity and sample using Markov chain Monte Carlo methods (MCMC). The main advantage of the method presented in this paper is the use of multiscale methods within an MCMC method based on Langevin diffusion. Additionally, we discuss techniques to combine multiscale methods with stochastic solution techniques, specifically sparse grid collocation methods. We show that the proposed algorithms dramatically reduce the computational cost associated with traditional Langevin MCMC methods while providing similar sampling performance. [Copyright &y& Elsevier]

Abstract: Formation properties are one of the key factors in numerical modeling of flow and transport in geologic formations in spite of the fact that they may not be completely characterized. The incomplete knowledge or uncertainty in the description of the formation properties leads to uncertainty in simulation results. In this study, the ensemble Kalman filter (EnKF) approach is used for continuously updating model parameters such as hydraulic conductivity and model variables such as pressure head while simultaneously providing an estimate of the uncertainty through assimilating dynamic and static measurements, without resorting to the explicit computation of the covariance or the Jacobian of the state variables. A two-dimensional example is built to demonstrate the capability of EnKF and to analyze its sensitivity with respect to different factors such as the number of realizations, measurement timings, and initial guesses. An additional example is given to illustrate the applicability of EnKF to three-dimensional problems and to examine the model predictability after dynamic data assimilation. It is found from these examples that EnKF provides an efficient approach for obtaining satisfactory estimation of the hydraulic conductivity field with dynamic measurements. After data assimilation the conductivity field matches the reference field very well, and different kinds of incorrect prior knowledge of the formation properties may also be rectified to a certain extent. [Copyright &y& Elsevier]

• Log-conductivity (Y) is modeled as a generalized sub-Gaussian (GSG) field. • Average longitudinal spreading is smaller for GSG than for Gaussian Y fields. • Plume stretching in GSG fields is enhanced with respect to Gaussian fields. • Local dispersion masks identification of impacts of non-Gaussianity on transport. • The GSG nature of Y can be difficult to identify relying only on transport metrics. There are increasing evidences that probability distributions and associated statistical moments of a variety of hydrogeological and soil science variables and their spatial increments display distinctive scale-dependent features that are not captured by a typical Gaussian model. A Generalized Sub-Gaussian (GSG) model is able to capture key aspects of this pattern. We present the results of a suite of computational analyses set in a Monte Carlo framework and aimed at assessing the impact of a GSG structure of log hydraulic conductivity (Y) on transport of a conservative solute through a three-dimensional bounded porous medium under steady-state saturated Darcy flow. Our results indicate that the longitudinal spreading of a plume is on average significantly smaller for Sub-Gaussian than for Gaussian Y fields. Otherwise, the velocity field arising from a Sub-Gaussian Y field induces enhanced plume stretching with respect to what can be observed in a Gaussian Y setting, this aspect potentially influencing the strength of solute mixing within these two types of conductivity domains. We also find that, in some cases, it may be difficult to identify the nature of the underlying conductivity field relying solely on observations of solute concentrations migrating within the system. In this regard, we show that the action of local dispersion tends to mask the influence of Sub-Gaussianity on major transport metrics. [ABSTRACT FROM AUTHOR]

• Variations in the support/observation scale of hydraulic conductivity induce changes in the information content of the associated Darcy fluxes. • Propagation of information about conductivity onto Darcy flux through the flow and mass balance equation can be quantified via information theory. • Flux data at large scales provide more information about fluxes at small scales than what can be observed with reference to conductivities. We rest on an Information Theory perspective and assess (i) the average information content and (i) the information shared between Darcy flux fields associated with fields of hydraulic conductivity (K) characterized by differing support (or measurement) scale. We treat hydraulic conductivity as a spatial random field, characterized by a given distribution and correlation structure. The latter is modeled through a truncated power law variogram (TPV), which explicitly takes into account a characteristic length scale of the support volume of K through a lower cutoff scale. We then frame our study in a numerical Monte Carlo context where groundwater flow is evaluated across a collection of realizations of hydraulic conductivity characterized by different values of the TPV parameters and subject to uniform in the mean flow. We quantify information through the Shannon entropy of the probability mass functions of the Darcy flux components as well as the mutual information and the multivariate mutual information respectively shared by pairs and triplets of Darcy flux components related to hydraulic conductivity fields evaluated at diverse scales and associated with various levels of heterogeneity. Partitioning of multivariate mutual information according to unique, redundant and synergetic contributions is also quantified. We found consistent trends (i) in the variation of the average information content with respect to the size of lower cutoff scale and (ii) in the way information is shared between pairs and triplets of Darcy flux components associated with diverse support scales of the underlying conductivities. [ABSTRACT FROM AUTHOR]

• This paper introduces the concept of Traveling Pilot Points (TRIPS) for solving the categorical inverse problem • The TRIPS methodology combined with subspace methods is used to identify channel structures in a categorical aquifer system while honoring calibration and prior constraints • Tests on a synthetic example suggest that the TRIPS methodology has promise and could be tested on more complicated problems Categorical parameter distributions are common-place in hydrogeological systems consisting of geologic facies/categories with distinct properties, e.g., high-permeability channels embedded in a low-permeability matrix. Parameter estimation is difficult in such systems because the discontinuities in the parameter space hinder the inverse problem. Previous research in this area has been focused on the use of stochastic methods. In this paper, we present a novel approach based on Traveling Pilot points (TRIPS) combined with subspace parameter estimation methods to generate realistic categorical parameter distributions that honor calibration constraints (e.g., - measured water levels). In traditional implementations, aquifer properties (e.g., hydraulic conductivity) are estimated at fixed pilot point locations. In the TRIPS implementation, both the properties associated with the pilot points and their locations are estimated. Tikhonov regularization constraints are incorporated in the parameter estimation process to produce realistic parameter depictions. For a synthetic aquifer system, we solved the categorical inverse problem by combining the TRIPS methodology with two subspace methods: Null Space Monte Carlo (NSMC) and Posterior Covariance (PC). A posterior ensemble developed with the rejection sampling (RS) method is compared against the TRIPS ensembles. The comparisons indicated similarities between the various ensembles and to the reference parameter distribution. Between the two subspace methods, the NSMC method produced an ensemble with more variability than the PC method. These preliminary results suggest that the TRIPS methodology has promise and could be tested on more complicated problems. [ABSTRACT FROM AUTHOR]

• Heterogeneous aquifers are modeled with log-normal random conductivity fields. • Numerical schemes are tested on two-dimensional benchmark flow problems. • Code verification performed by comparison with analytical manufactured solutions. • Validation done through comparisons between MC statistics and first-order results. • Limited computational feasibility for exponentially correlated log-conductivity. This article presents numerical investigations on accuracy and convergence properties of several numerical approaches for simulating steady state flows in heterogeneous aquifers. Finite difference, finite element, discontinuous Galerkin, spectral, and random walk methods are tested on two-dimensional benchmark flow problems. Realizations of log-normal hydraulic conductivity fields are generated by Kraichnan algorithms in closed form as finite sums of random periodic modes, which allow direct code verification by comparisons with manufactured reference solutions. The quality of the methods is assessed for increasing number of random modes and for increasing variance of the log-hydraulic conductivity fields with Gaussian and exponential correlation. Experimental orders of convergence are calculated from successive refinements of the grid. The numerical methods are further validated by comparisons between statistical inferences obtained from Monte Carlo ensembles of numerical solutions and theoretical first-order perturbation results. It is found that while for Gaussian correlation of the log-conductivity field all the methods perform well, in the exponential case their accuracy deteriorates and, for large variance and number of modes, the benchmark problems are practically not solvable with reasonably large computing resources, for all the methods considered in this study. [ABSTRACT FROM AUTHOR]