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The use of spreadsheets in numerical groundwater flow modeling, although not new, has yet to be fully exploited in educational settings. This teaching aid complements the one published earlier, broadening its scope from the numerical solution for a single-layer confined aquifer to include unconfined aquifers. It covers flow in horizontal layers and multilayered vertical cross-sections. The new spreadsheets can also handle hydraulic conductivity anisotropy, provided its principal directions align with the Cartesian axes. It can also manage irregular aquifer shapes, incorporate all three types of boundary conditions, and account for pumping and recharge in the modeling process. However, the flow regime remains at steady state. With the integration of these new features, the spreadsheet model becomes a versatile tool for simulating a great number of steady-state groundwater flow situations. This user-friendly platform enables students to swiftly experiment with diverse configurations, delve into the underlying mathematical expressions, and gain insight into the workings of more sophisticated numerical codes like MODFLOW. As a complement, and for verification purposes, Python Flopy scripts are provided for all examples in the GitHub repository where the spreadsheets can be accessed. [ABSTRACT FROM AUTHOR]

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Aquifers are ubiquitous, and their water is easy to obtain with low extraction costs. On many occasions, these characteristics lead to overexploitation due to important water level declines, reduction of river base flows, enhanced seawater intrusion, and wetland affection. The forecasted increase in water demands and global warming will impact the future availability of water resources. Conjunctive use of surface and subsurface waters can help in mitigating these impacts. There are two main conjunctive use strategies: artificial recharge (AR) and alternate conjunctive use (ACU). AR stores waters that are not to be used directly in aquifers. ACU utilizes groundwater in dry periods, while surface waters are preferred in wet ones; this allows the increase of water supply with lower dam storage, economic gains, and environmental advantages. Efficient conjunctive use can prevent soil salinization and waterlogging problems in semiarid countries due to excessive recharge from irrigation return flows or other origins. Groundwater is a neglected and generally misused resource to maintain environmental conditions. When considering the solution to a water resources problem, groundwater should always be part of the design as an alternative or a complementary resource. Aquifers have large inertia, and changes in their volumes are only noticeable after years of observations. Unfortunately, groundwater observation networks are much poorer than surface ones, something that should be changed if groundwater is to come to the rescue in these times of climate change. Human and material resources should be made available to monitor, control, analyze, and forecast groundwater. [ABSTRACT FROM AUTHOR]

The joint identification of the parameters defining a contaminant source and the heterogeneous distribution of the hydraulic conductivities of the aquifer where the contamination took place is a difficult task. Previous studies have demonstrated the applicability of the restart normal-score ensemble Kalman filter (rNS-EnKF) in synthetic cases making use of sufficient hydraulic head and concentration data. This study shows an application of the same technique to a non-synthetic case under laboratory conditions and discusses the difficulties found on its application and the avenues taken to solve them. The method is first tested using a synthetic case that mimics the sandbox experiment to establish the minimum number of ensemble members and the best technique to prevent the filter collapsing. The synthetic case shows that among different techniques based on update damping and covariance inflation, the Bauser's covariance inflation method works best in preventing filter collapse. Its application to the sandbox data shows that the rNS-EnKF can benefit from Bauser's inflation to reduce the number of ensemble realizations substantially in comparison with a filter without inflation, arriving at a good joint identification of both the contaminant source and the spatial heterogeneity of the conductivities. [ABSTRACT FROM AUTHOR]

Early detection of a contamination leach into a water distribution system, followed by the identification of the source and an evaluation of the total amount of the contaminant that has been injected into the system, is of paramount importance in order to protect a water user's health. The ensemble Kalman filter, which has been recently applied in hydrogeology to detect contaminant sources in aquifers, is extended to the identification of a contaminant source and its intensity in a water distribution system. The driving concept is the assimilation of contaminant observations at the nodes of the pipeline network at specified time intervals until enough information has been collected to allow the positioning of the source and the estimation of its intensity. Several scenarios are analyzed considering sources at different nodes, with different delays between the beginning of the pollution and the start of the measurements, different sampling time intervals, and different observation ending times. The scenarios are carried out in the benchmarking Anytown network, demonstrating the ability of the ensemble Kalman filter for contaminant-source detection in real water distribution systems. The use of the ensemble Kalman filter supposed a major breakthrough in the inverse modeling of subsurface flow and transport, and the successful results of its application to the synthetic Anytown network warrant further exploration of its capabilities in the realm of water distribution systems. [ABSTRACT FROM AUTHOR]

Detecting where and when a contaminant entered an aquifer from observations downgradient of the source is a difficult task; this identification becomes more challenging when the uncertainty about the spatial distribution of hydraulic conductivity is accounted for. In this paper, we have implemented an application of the restart normal-score ensemble Kalman filter (NS-EnKF) for the simultaneous identification of a contaminant source and the spatially variable hydraulic conductivity in an aquifer. The method is capable of providing estimates of the spatial location, initial release time, the duration of the release and the mass load of a point-contamination event, plus the spatial distribution of hydraulic conductivity together with an assessment of the estimation uncertainty of all the parameters. The method has been applied in synthetic aquifers exhibiting both Gaussian and non-Gaussian patterns. The identification is made possible by assimilating in time both piezometric head and concentration observations from an array of observation wells. The method is demonstrated in three different synthetic scenarios that combine hydraulic conductivities with unimodal and bimodal histograms, and releases in high and low conductivity zones. The results prove that the specific implementation of the EnKF is capable of recovering the source parameters with some uncertainty and of recovering the main patterns of heterogeneity of the hydraulic conductivity fields by assimilating a sufficient number of state variable observations. The proposed approach is an important step towards contaminant source identification in real aquifers, which may have logconductivity spatial distributions with either Gaussian or non-Gaussian features, yet, it is still far from practical applications since the transport parameters, the external sinks and sources and the initial and boundary conditions are assumed known. [ABSTRACT FROM AUTHOR]

The article discusses papers presented in the AGU Chapman Conference ‘The MADE Challenge for Groundwater Transport in Highly Heterogeneous Aquifers: Insights from 30 Years of Modeling and Characterization at the Field Scale and Promising Future Directions' in Valencia, Spain in October 2015.

State data (piezometric head) provides valuable information for identifying the spatial pattern of aquifer parameters (hydraulic conductivity) and to reduce the uncertainty of aquifer models. To extract such spatial information from the measurements and accurately quantify the uncertainty, a Monte Carlo method typically calls for a large number of realizations that are conditional to hard data and inverse-conditional to state data. However, inverse stochastic simulation is extremely computationally intensive, since a non-linear inverse problem is involved. In contrast to some classical non-linear optimizers, a blocking Markov chain Monte Carlo scheme is presented to generate independent, identically distributed realizations by sampling directly from a posterior distribution that incorporates a priori information and a posteriori observations in a Bayesian framework. The realizations are not only conditioned to hard data and inverse-conditioned to state data, but also preserve expected spatial structures. A synthetic example demonstrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• The ES-MDA is used to jointly identify contaminant source and conductivities. • The r-EnKF is used as a benchmark for comparison. • The results show the ES-MDA could be superior to the r-EnKF after enough iterations. Understanding a contaminant source may help in a better management and risk assessment of a polluted aquifer. However, contaminant source information may not be available when a pollutant is detected in a drinking well. The restart ensemble Kalman filter (restart EnKF, also named r-EnKF) has been demonstrated in synthetic and laboratory experiments as an efficient solution for the identification of a contaminant source. Recently, the ensemble smoother with multiple data assimilation (ES-MDA) has been proposed as an alternative to the r-EnKF as a more efficient solution given that the r-EnKF needs to restart the simulation of the state equation from time zero after each data assimilation step. An analysis, in a synthetic aquifer, of the accuracy of the ES-MDA for the simultaneous identification of a contaminant source and the spatial distribution of hydraulic conductivity by assimilating both piezometric head and concentration observations is carried out using the r-EnKF as a benchmark. The conclusion is that the ES-MDA can outperform the r-EnKF, but the expected speed advantage, associated with the possibility of assimilating all data at once, does not exist. For the ES-MDA to reach the same level of accuracy as the r-EnKF, the number of multiple data assimilations must be large, and final computing time is similar for both approaches. However, the ES-MDA can do much better than the r-EnKF if the number of iterations increases even further, with the consequent increase of computational cost. [ABSTRACT FROM AUTHOR]

Contaminant source and aquifer characterization (CSAC) is critical in groundwater pollution evaluation and remediation. The ensemble smoother with multiple data assimilation (ES-MDA) is utilized to jointly identify contaminant source information and hydraulic conductivities by assimilating time-lapse electrical resistivity tomography (ERT) data. In a synthetic profile with a time-varying release history in a heterogeneous aquifer, we verify the performance of the proposed data assimilation framework. The results show that the CSAC problem could be handled by the proposed approach. The time-varying release history and the high permeability area can be identified with adequate time-lapse ERT measurements. Further reproduction of the evolution of the plume after CSAC also shows consistency with the reference plume. The poor conditioning inversion caused by the filter inbreeding is analyzed by comparing four scenarios with different apparent resistivity measurements. Furthermore, we also evaluate the impact of uncertainties in the petrophysical properties and geophysical observations on our data assimilation framework. The results show that the proposed ES-MDA data assimilation framework could provide a convincing inversion of the time-varying release history and hydraulic conductivities. • We propose a data assimilation framework including the coupling of the simulators of MODFLOW, MT3DS and ResIPy into the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) implementation. • The time-lapse electrical resistivity tomography (ERT) measurements are first employed for the joint identification of contaminant source information and hydraulic conductivities. • We analyze the influence of different bipole–bipole electrode array configurations on the ES-MDA data assimilation framework. [ABSTRACT FROM AUTHOR]

• The ES-MDA is used to identify non-point contaminant sources. • An ellipse is updated to approximate the (ir-)regular non-point source area. • The results show the ES-MDA can deal with non-point problems after enough iterations. Proper identification of groundwater contaminant sources is vital to assess groundwater contamination. However, the majority of previous studies focuses on point source identification; only a few works have been conducted for non-point source parameter identification. Here, we employ the ensemble smoother with multiple data assimilation (ES-MDA) to simultaneously identify the spatial architecture of non-point contaminant sources and the related release information. Three different shapes of non-point contaminant sources are considered, an ellipse, a circle, and an irregular shape. We test the applicability of the ES-MDA for the simultaneous identification using three scenarios in a synthetic confined aquifer by assimilating concentration observations from all-time steps multiple times. The results demonstrate that the ES-MDA is capable to accurately identify both regular and irregular non-point contaminant source information; the accuracy of the identification can be improved by increasing the number of iterations. [ABSTRACT FROM AUTHOR]

Space-temporal distribution of the contaminant plumes and aquifer properties is critical for groundwater management. However, most previous studies have focused on point source identification, barely exploring the identification of non-point sources. Xu et al. (2022) proposed to identify non-point sources but did not consider uncertainties in aquifer properties and release mass loading. In this work, we have implemented an application of the localized ensemble smoother with multiple data assimilation (LES-MDA) for the simultaneous identification of Gaussian hydraulic conductivities and non-point source parameters including Gaussian release mass-loading by assimilating both piezometric head and concentration observations in a synthetic confined aquifer. The results prove that the LES-MDA is not only capable of providing accurate identification of the spatial architecture of non-point contaminant sources and related release parameters (such as initial release time, and release duration) but also spatially heterogeneous release mass-loading and hydraulic conductivities. • Localized ES-MDA jointly identifies non-point source and conductivity field. • Uncertainty in the spatial distribution of field and mass-loading is considered. • Three scenarios with two different techniques test the joint identification. • Combining localization reduces filter inbreeding and spurious correlation. [ABSTRACT FROM AUTHOR]

• Joint recovery of source location and release history of a pollutant in groundwater. • An ensemble Kalman filter method is used to estimate time-series. • A good observation sampling scheme mitigates ill-posedness and equifinality. • Covariances are corrected by means of a spatiotemporal iterative localization. • The methodology is validated using real data collected in a laboratory sandbox. The source location and the time history of a pollutant released in an aquifer are very relevant information for the design of effective remediation strategies. Usually, their identification requires solving an inverse problem when the only available information about the groundwater contamination event is a sparse set of concentration data collected in the aquifer at a few points downstream from the source. Here, a novel approach is proposed to solve the inverse problem: the use of the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) in the context of source contamination identification. This method is used for the simultaneous determination of the time history and the source location of a pollutant release based on observed concentration data and a calibrated numerical model of groundwater flow and mass transport in the aquifer. The ES-MDA is demonstrated in two case studies. The first one is based on an analytical solution of the flow and transport equations, aimed at the estimation of the source location and the release history of a nonreactive pollutant spreading in a two-dimensional homogeneous aquifer from a point source. For this case, different alternatives are considered for the spatial distribution of the observation points, the concentration sampling frequency, the ensemble size and the use of covariance localization and covariance inflation techniques in the formulation of the smoother. The purpose of this case is to test the new approach, analyze its performance and also to identify the conditions that render the problem ill-posed and, therefore, without solution; also, in this case, a new spatiotemporal iterative localization is presented. In the second case study, we use real data collected in a laboratory sandbox that reproduces a vertical cross-section of an unconfined aquifer with two-dimensional quasi-parallel flow between constant-head boundaries. The results show that the location, time and number of observations, the ensemble size and the application of covariance localization and covariance inflation techniques have an impact on the final solution. A well-designed monitoring network and the application of covariance corrections improve the performance of the ES-MDA and help avoiding ill-posedness and equifinality. The application to laboratory data validates the potential of ES-MDA to simultaneously estimate the time history and the source location of a pollutant released in groundwater in real cases. [ABSTRACT FROM AUTHOR]