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Abstract Identification of preferential flow paths in heterogeneous subsurface environments is key to assess early solute arrival times at environmentally sensitive targets. We propose a novel methodology that leverages the information contained in preferential flow paths to quantify early arrival times and their associated uncertainty. Our methodology is based on a two‐stage approach that combines Convolutional Neural Networks (CNN) and Multi‐Layer Perceptron (MLP) techniques. The CNN is used to identify preferential flow paths, the MLP being employed to map tortuosity of these paths and key geostatistical parameters of conductivities therein onto early arrival times. As such, our approach provides novel insights into the relationship between the geostatistical characterization of conductivities along preferential flow paths and early arrival times. The effectiveness of the approach is exemplified on synthetic two‐dimensional (randomly) heterogeneous hydraulic conductivity fields. In this context, we assess three distinct CNN architectures and two MLP architectures to determine the most effective combination between these to reliably and effectively quantifying preferential flow paths and early arrival times of solutes. The resulting framework is robust and efficient. It enhances our ability to assess early solute arrival times in heterogeneous aquifers and offers valuable insights into connectivity patterns associated with preferential flow paths therein. 

Abstract Managed Aquifer Recharge (MAR) plays an important role in improving and supplementing groundwater storage. Many natural factors, ranging from climatic conditions to soil characteristics, can impact the efficiency of an infiltration basin. Other factors, such as engineered variables, will also influence the basin performance and the risks associated with groundwater contamination. The latter depends on the interplay between the hydraulic characteristics of the system and the soil and solute properties. The design of infiltration basins has been performed so far with the main objective of mitigating the tendency of the basin to reduce the infiltration rate with time due to clogging of the basin's bottom. Less attention has been paid to the risk of groundwater contamination by the infiltrating water. To understand the complex interplay between natural and engineering parameters on MAR efficiency and the contamination risk, we propose a risk‐oriented analytical framework. The framework allows to investigate the interplay between soil parameters, engineering design and climatic factors on the efficiency of an infiltration basin. Our framework relies on novel analytical solutions that relates the geometrical and hydrological features of the infiltration basin to its efficiency and groundwater contamination risk. The solutions incorporates the randomness associated with inflows (precipitation) and soil properties. We explore the trade‐off between efficiency and the risk of contamination and delineate a design procedure that balances these two opposing needs. Although the framework relies on simplifying assumptions, it provides a computationally efficient manner to obtain physical insights and relate model input parameters to decision making. 

Abstract The fate of nutrients and contaminants in fluvial ecosystems is strongly affected by the mixing dynamics between surface water and groundwater within the hyporheic zone, depending on the combination of the sediment's hydraulic heterogeneity and dune morphology. This study examines the effects of hydraulic conductivity stratification on steady‐state, two‐dimensional, hyporheic flows and solute residence time distribution. First, we derive an integral transform‐based semi‐analytical solution for the flow field, capable of accounting for the effects of any functional shape of the vertically varying hydraulic conductivity. The solution considers the uneven distribution of pressure at the water‐sediment interface (i.e., the pumping process) dictated by the presence of dune morphology. We then simulate solute transport using particle tracking. Our modeling framework is validated against numerical and tracer data from flume experiments and used to explore the implication of hydraulic conductivity stratification on the statistics andpdfof the residence time. Finally, reduced‐order models are used to enlighten the dependence of key residence time statistics on the parameters characterizing the hydraulic conductivity stratification. 

Abstract The presence of well‐connected paths is commonly observed in spatially heterogeneous porous formations. Channels consisting of high hydraulic conductivity (K) values strongly affect fate and transport of dissolved species in the subsurface environment. Several studies have established a correlation between connectivity properties of the spatially variableK‐field and solute first arrival times. However, due to limited knowledge of the spatial structure of theK‐field, connectivity metrics are subject to uncertainty. In this work, we utilize the concept of the minimum hydraulic resistance and least resistance path to evaluate the connectivity of aK‐field in a stochastic framework. We employ a fast graph theory‐based algorithm to alleviate the computational burden associated with stochastic computations in order to investigate both the impact of the hydrogeological structural conceptualization and domain dimensionality (2‐D vs. 3‐D) on the uncertainty of the minimum hydraulic resistance. Finally, we propose an iterative data acquisition strategy that can be utilized to identify the least resistance path (which is linked to preferential flow channels) in real sites. A synthetic benchmark test is presented, showing the advantages of the proposed sampling strategy when compared to a regular sampling strategy. By using the iterative data sampling strategy, we were able to reduce first arrival time uncertainty by 47% (when compared to the regular sampling strategy), while maintaining site characterization efforts constant. 

Abstract An inadequate characterization of hydrogeological properties can significantly decrease the trustworthiness of subsurface flow and transport model predictions. A variety of data assimilation methods have been proposed in order to estimate hydrogeological parameters from spatially scarce data by incorporating them into the governing physical models. In order to quantify the accuracy of the estimations, several metrics have been used such as Rank Histograms, root‐mean‐square error (RMSE), and Ensemble Spread. However, these commonly used metrics do not regard the spatial correlation of the aquifer's properties. This can cause permeability fields with very different spatial structures to have similar histograms or RMSE. In this paper, we propose an approach based on color coherence vectors (CCV) for evaluating the performance of these estimation methods. CCV is a histogram‐based technique for comparing images that incorporate spatial information. We represent estimated fields as digital three‐channel images and use CCV to compare and quantify the accuracy of estimations. The appealing feature of this technique is that it considers the spatial structure embedded in the estimated fields. The sensitivity of CCV to spatial information makes it a suitable metric for assessing the performance of data assimilation techniques. Under various factors, such as numbers of measurements and structural parameters of the log conductivity field, we compare the performance of CCV with the RMSE. 

Obtaining accurate and deterministic predictions of the risks associated with the presence of contaminants in aquifers is an illusive goal given the presence of heterogeneity in hydrological properties and limited site characterization data. For such reasons, a probabilistic framework is needed to quantify the risks in groundwater systems. In this work, we present a computational toolboxVisU-HydRAthat aims to statistically characterize and visualize metrics that are relevant in risk analysis with the ultimate goal of supporting decision making. TheVisU-HydRAcomputational toolbox is an open-source Python package that can be linked to a series of existing codes such as MODFLOW and PAR², a GPU-accelerated transport simulator. To illustrate the capabilities of the computational toolbox, we simulate flow and transport in a heterogeneous aquifer within a Monte Carlo framework. The computational toolbox allows to compute the probability of a contaminant’s concentration exceeding a safe threshold value as well as the uncertainty associated with the loss of resilience of the aquifer. To ensure consistency and a reproducible workflow, a step-by-step tutorial is provided and available on a GitHub repository.