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Water-table maps are fundamental to hydrogeological studies and a manual, hand-drawn method is still commonly used to produce them. Despite this, the accuracy and variability of such maps have received little attention in international literature. In a unique experiment, 63 groundwater professionals drew water-table equipotential contours based on the same dataset of point measurements and were asked to infer flow directions and predict groundwater elevations at predefined locations. The root mean squared error (RMSE) for the average map calibration data was 10.5 m, which is accuracy comparable to numerical groundwater models. This study confirmed that to produce hand-drawn water-table maps, practitioners seek to not only fit the spatial data, but also to conform to their own cognitive model of hydrogeological concepts and processes. The calibration accuracy increased with experience; from a RMSE of 13.3 m for practitioners with 0-3 years of experience to a RMSE of 9.2 m for those with four or more years. Despite considerable variability in the style of the hand-drawn water-table maps, the maps were consistent in their representation of the dominant regional groundwater flow directions. There was less consensus, however, in predicting the direction of surface water-groundwater interaction for a stream reach. Hand-drawn water-table mapping remains useful and valid, especially as a starting point for hydrogeological conceptualization, yet further work is required to resolve issues around transparency, repeatability, and reproducibility., (© 2024 Commonwealth of Australia and The Authors. Groundwater published by Wiley Periodicals LLC on behalf of National Ground Water Association.)

This paper presents the Kalman Filtered Double Constraint Method (DCM-KF) as a technique to estimate the hydraulic conductivities in the grid blocks of a groundwater flow model. The DCM is based on two forward runs with the same initial grid block conductivities, but with alternating flux-head conditions specified on parts of the boundary and the wells. These two runs are defined as: (1) the flux run, with specified fluxes (recharge and well abstractions), and (2) the head run, with specified heads (measured in piezometers). Conductivities are then estimated as the initial conductivities multiplied by the fluxes obtained from the flux run and divided by the fluxes obtained from the head run. The DCM is easy to implement in combination with existing models (e.g., MODFLOW). Sufficiently accurate conductivities are obtained after a few iterations. Because of errors in the specified head-flux couples, repeated estimation under varying hydrological conditions results in different conductivities. A time-independent estimate of the conductivities and their inaccuracy can be obtained by a simple linear KF with modest computational requirements. For the Kleine Nete catchment, Belgium, the DCM-KF yields sufficiently accurate calibrated conductivities. The method also results in distinguishing regions where the head-flux observations influence the calibration from areas where it is not able to influence the hydraulic conductivity., (© 2014, National Ground Water Association.)

The prediction of the location of ground water discharge areas is a key aspect for the protection and (re)development of ground water-dependent wetlands. Ground water discharge areas can be simulated with MODFLOW using the DRAIN package by setting the drain level equal to the topography, while the conductance is mostly set to an arbitrary high value. However, conceptual and practical problems arise in the calculation of the ground water discharge by the DRAIN package as calculated water tables above the land surface, difficult parameterization of the conductance, and large water balance errors. To overcome these problems, a new SEEPAGE package for MODFLOW is proposed. The basic idea of this package is an adaptable constant head cell. It has a variable head, unless the ground water rises above the seepage level, in which case it has a constant head cell. The estimation of the ground water discharge location along a homogeneous, isotropic, linear sloping profile is used to verify the model and to compare it to the DRAIN solution. In an application to three basins in Belgium, it is shown that the SEEPAGE package can be used in combination with the DRAIN package in situations where an upper boundary for a free water table and additional resistance for drainage is required. It is clearly demonstrated that the identification and delineation of regional ground water discharge areas is more accurate using the SEEPAGE package.

This paper presents a Bayesian Monte Carlo method for evaluating the uncertainty in the delineation of well capture zones and its application to a wellfield in a heterogeneous, multiaquifer system. In the method presented, Bayes' rule is used to update prior distributions for the unknown parameters of the stochastic model for the hydraulic conductivity, and to calculate probability-based weights for parameter realizations using head residuals. These weights are then assigned to the corresponding capture zones obtained using forward particle tracking. Statistical analysis of the set of weighted protection zones results in a probability distribution for the capture zones. The suitability of the Bayesian stochastic method for a multilayered system is investigated, using the wellfield Het Rot at Nieuwrode, Belgium, located in a three-layered aquifer system, as an example. The hydraulic conductivity of the production aquifer is modeled as a spatially correlated random function with uncertain parameters. The aquitard and overlying unconfined aquifer are assigned random, homogeneous conductivities. The stochastic results are compared with deterministic capture zones obtained with a calibrated model for the area. The predictions of the stochastic approach are more conservative and indicate that parameter uncertainty should be taken into account in the delineation of well capture zones.