Keywords:Uncertainty Contaminant transport Modeling Braided river Aquifer Groundwater s u m m a r y Hydrogeologist are commonly confronted to field data scarcity. An interesting way to compensate this data paucity, is to use analog data. Then the questions of prediction accuracy and uncertainty assessment when using analog data shall be raised. These questions are investigated in the current paper in the case of contaminant transport forecasting in braided river aquifers. In using analog data from the literature, multiple unconditional geological realizations are produced following different geological conceptual models (Multi-Gaussian, Object-based, Pseudo-Genetic). These petrophysical realizations are tested in a contaminant transport problem based on the MADE-II tracer experiment dataset. The simulations show that reasonable contaminant transport predictions can be achieved using analog data. The initial concentration conditions and location regarding the conductivity heterogeneity field have a stronger influence on the plume behavior than the resulting equivalent permeability. The results also underline the necessity to include a wide variety of geological conceptual models and not to restrain parameter space exploration within each concept as long as no field data allows for conceptual model or parameter value falsification.

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Hydrogeological flow and transport strongly depend on the connectivity of subsurface properties. Uncertainty concerning the underlying geological setting, due to a lack of field data and prior knowledge, calls for an evaluation of alternative geological conceptual models. To reduce the computational costs associated with inversions (parameter estimation for a given conceptual model), it is beneficial to rank and discard unlikely conceptual models prior to inversion. Here, we demonstrate an approach based on a quantitative comparison of groundpenetrating radar (GPR) sections obtained from field data with corresponding simulation results arising from various geological scenarios. The comparison is based on three global distance measures related to wavelet decomposition, multiple-point histograms, and connectivity that capture geometrical characteristics of geophysical reflection images. Using field data from the Tagliamento braided river system, Italy, we demonstrate that seven out of nine considered geological scenarios can be discarded as they produce GPR sections that are incompatible with those observed in the field. The retained scenarios reproduce important features such as cross-stratified deposits and irregular property interfaces. The most convenient distance measure of those considered is the one based on wavelet-decomposition. Direct analysis of the distances is the most intuitive and fastest way to compare scenarios.

Abstract. Coarse, braided river deposits show a large hydraulic heterogeneity on the metre scale. One of the main depositional elements found in such deposits is a trough structure filled with layers of bimodal gravel and open-framework gravel, the latter being highly permeable. However, the impact of such trough fills on subsurface flow and advective mixing has not drawn much attention. A geologically realistic model of trough fills is proposed and fitted to a limited number of ground-penetrating radar records surveyed on the river bed of the Tagliamento River (northeast Italy). A steady-state, saturated subsurface flow simulation is performed on the small-scale, high-resolution, synthetic model (size: 75 m × 80 m × 9 m). Advective mixing (i.e. streamline intertwining) is visualised and quantified based on particle tracking. The results indicate strong advective mixing as well as a large flow deviation induced by the asymmetry of the trough fills with regard to the main flow direction. The flow deviation induces a partial, large-scale rotational effect. These findings depict possible advective mixing found in natural environments and can guide the interpretation of ecological processes such as in the hyporheic zone.

The time (vertical) resolution enhancement of ground-penetrating radar (GPR) data by deconvolution is a longstanding problem due to the mixed-phase characteristics of the source wavelet. Several approaches have been proposed, which take the mixed-phase nature of the GPR source wavelet into account. However, most of these schemes are usually laborious and/or computationally intensive and have not yet found widespread use. Here, we propose a simple and fast approach to GPR deconvolution that requires only a minimal user input. First, a trace-by-trace minimum-phase (spiking) deconvolution is applied to remove the minimum-phase part of the mixed-phase GPR wavelet. Then, a global phase rotation is applied to maximize the sparseness (kurtosis) of the minimum-phase deconvolved data to correct for phase distortions that remain after the minimum-phase deconvolution. Applications of this scheme to synthetic and field data demonstrate that a significant improvement in image quality can be achieved, leading to deconvolved data that are a closer representation of the underlying reflectivity structure than the input or minimum-phase deconvolved data. Synthetic-data tests indicate that, because of the temporal and spatial correlation inherent in the GPR data due to the frequency-and wavenumber-bandlimited nature of the GPR source wavelet and the reflectivity structure, a significant number of samples are required for a reliable sparseness (kurtosis) estimate and stable phase rotation. This observation calls into question the blithe application of kurtosis-based methods within short time windows such as that for time-variant deconvolution.

This paper, based on a real world case study (Limmat aquifer, Switzerland), compares inverse groundwater flow models calibrated with specified numbers of monitoring head locations. These models are updated in real time with the ensemble Kalman filter (EnKF) and the prediction improvement is assessed in relation to the amount of monitoring locations used for calibration and updating. The prediction errors of the models calibrated in transient state are smaller if the amount of monitoring locations used for the calibration is larger. For highly dynamic groundwater flow systems a transient calibration is recommended as a model calibrated in steady state can lead to worse results than a noncalibrated model with a well-chosen uniform conductivity. The model predictions can be improved further with the assimilation of new measurement data from on-line sensors with the EnKF. Within all the studied models the reduction of 1-day hydraulic head prediction error (in terms of mean absolute error [MAE]) with EnKF lies between 31% (assimilation of head data from 5 locations) and 72% (assimilation of head data from 85 locations). The largest prediction improvements are expected for models that were calibrated with only a limited amount of historical information. It is worthwhile to update the model even with few monitoring locations as it seems that the error reduction with EnKF decreases exponentially with the amount of monitoring locations used. These results prove the feasibility of data assimilation with EnKF also for a real world case and show that improved predictions of groundwater levels can be obtained.

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