Abstract. Estimating the spatial variability of hydraulic conductivity K in natural aquifers is important for predicting the transport of dissolved compounds. Especially in the nonreactive case, the plume evolution is mainly controlled by the heterogeneity of K. At the local scale, the spatial distribution of K can be inferred by combining the Lagrangian formulation of the transport with a Kalman-filter-based technique and assimilating a sequence of time-lapse concentration C measurements, which, for example, can be evaluated on site through the application of a geophysical method. The objective of this work is to compare the ensemble Kalman filter (EnKF) and the ensemble smoother (ES) capabilities to retrieve the hydraulic conductivity spatial distribution in a groundwater flow and transport modeling framework. The application refers to a two-dimensional synthetic aquifer in which a tracer test is simulated. Moreover, since Kalmanfilter-based methods are optimal only if each of the involved variables fit to a Gaussian probability density function (pdf) and since this condition may not be met by some of the flow and transport state variables, issues related to the nonGaussianity of the variables are analyzed and different transformation of the pdfs are considered in order to evaluate their influence on the performance of the methods. The results show that the EnKF reproduces with good accuracy the hydraulic conductivity field, outperforming the ES regardless of the pdf of the concentrations.

The significance of estimating the spatial variability of the hydraulic conductivity <i>K</i> in natural aquifers is relevant to the possibility of defining the space and time evolution of a non-reactive plume, since the transport of a solute is mainly controlled by the heterogeneity of <i>K</i>. At the local scale, the spatial distribution of <i>K</i> can be inferred by combining the Lagrangian formulation of the transport with a Kalman filter-based technique and assimilating a sequence of time-lapse concentration <i>C</i> measurements, which, for example, can be evaluated on-site through the application of a geophysical method. The objective of this work is to compare the ensemble Kalman filter (EnKF) and the ensemble smoother (ES) capabilities to retrieve the hydraulic conductivity spatial distribution in a groundwater flow and transport modeling framework. The application refers to a two-dimensional synthetic aquifer in which a tracer test is simulated. Moreover, since Kalman filter-based methods are optimal only if each of the involved variables fit to a Gaussian probability density function (pdf) and since this condition may not be met by some of the flow and transport state variables, issues related to the non-Gaussianity of the variables are analyzed and different transformation of the pdfs are considered in order to evaluate their influence on the performance of the methods. The results show that the EnKF reproduces with good accuracy the hydraulic conductivity field, outperforming the ES regardless of the pdf of the concentrations

No abstract

Seawater intrusion in coastal aquifers is a worldwide problem exacerbated by aquifer overexploitation and climate changes. To limit the deterioration of water quality caused by saline intrusion, research studies are needed to identify and assess the performance of possible countermeasures, e.g., underground barriers. Within this context, numerical models are fundamental to fully understand the process and for evaluating the effectiveness of the proposed solutions to contain the saltwater wedge; on the other hand, they are typically affected by uncertainty on hydrogeological parameters, as well as initial and boundary conditions. Data assimilation methods such as the ensemble Kalman filter (EnKF) represent promising tools that can reduce such uncertainties. Here, we present an application of the EnKF to the numerical modeling of a laboratory experiment where seawater intrusion was reproduced in a specifically designed sandbox and continuously monitored with electrical resistivity tomography (ERT). Combining EnKF and the SUTRA model for the simulation of density-dependent flow and transport in porous media, we assimilated the collected ERT data by means of joint and sequential assimilation approaches. In the joint approach, raw ERT data (electrical resistances) are assimilated to update both salt concentration and soil parameters, without the need for an electrical inversion. In the sequential approach, we assimilated electrical conductivities computed from a previously performed electrical inversion. Within both approaches, we suggest dual-step update strategies to minimize the effects of spurious correlations in parameter estimation. The results show that, in both cases, ERT data assimilation can reduce the uncertainty not only on the system state in terms of salt concentration, but also on the most relevant soil parameters, i.e., saturated hydraulic conductivity and longitudinal dispersivity. However, the sequential approach is more prone to filter inbreeding due to the large number of observations assimilated compared to the ensemble size.

Salt-water intrusion (SWI) is a worldwide problem increasingly affecting coastal aquifers, exacerbated by climate changes and growing demand of fresh-water. Therefore, research on this topic using both physical and numerical modeling has been intensified, aiming to achieve better predictions of the salt-water wedge evolution and to design suitable countermeasures to its negative effects. This work presents a laboratory facility designed to conduct SWI experiments that can be used as benchmarks for numerical models. To this end, the laboratory facility has been designed to limit errors and provide redundant measurements of hydraulic heads and discharged flow rates. Moreover, the size of the facility allows us to monitor the salt-water wedge evolution by a specifically designed electrical resistivity tomography (ERT) monitoring system. To demonstrate the capabilities of the laboratory facility, we carried out a simple 36-h long SWI experiment in a homogeneous porous medium: during the initial 24 h the salt-water wedge evolved without any external forcing, while in the last 12 h, fresh-water was pumped out to simulate aquifer exploitation. The experiment was monitored through ERT and photos of the salt-water wedge collected at regular time intervals. The SUTRA code was used to reproduce the experimental results, by calibrating only the dispersivities. The ERT results show a good correlation with simulated concentrations between the borehole electrodes, the most sensitive zone of the monitored area, demonstrating that ERT can be used for laboratory evaluations of the salt-water evolution. Overall, the agreement between observed data, numerical simulations, and ERT results demonstrates that the proposed laboratory facility can provide valuable benchmarks for future studies of SWI, even in more complex settings.