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This study provides a kinematic explanation for why facies interfaces dominate solute transport in heterogeneous aquifers. Using flow and transport simulations, we apply kinematic metrics to quantify deformation processes that control plume evolution. Results show that strong conductivity contrasts generate preferential flow corridors, while transitional zones at facies interfaces act as persistent mixing fronts where stretching and folding intensify mixing. These cross-facies transitions emerge as the primary controls on transport observables such as dispersion and dilution, with within-facies variability exerting secondary effects. By linking sedimentary architecture to flow deformation, this work provides the mechanistic justification for earlier findings that cross-transition probabilities govern solute spreading. The results highlight the need to resolve geologic interfaces in both field characterization and remediation design. Flow topology offers a unifying framework for predicting transport in aquifers and points to opportunities for geophysical methods to target the key architectural features that regulate mixing and dilution. 

Heterogeneity of soil hydraulic (e.g., hydraulic conductivity (KS), porosity (θS)) and chemical (e.g., solid-phase adsorption (Kd)) properties complicates contaminant transport by creating spatial variability in sources of contaminant leaching. There is a knowledge gap on the effect of the interplay between these properties on the retardation and transport of per- and polyfluoroalkyl substances (PFAS) with different properties including carbon–fluorine chain-length and functional groups even in water-saturated conditions. Breakthrough curves have been used to evaluate PFAS transport behavior through heterogeneous media, including arrival time, maximum concentration, and tailing behavior. Contaminant mass flux reduction and mass removal correlations are also compared using numerical modeling to characterize PFAS transport through different source zones within a two-domain, heterogeneous system with comparison to homogeneous scenarios under water-saturated conditions. With heterogeneous properties, model sensitivity to KS was the highest among the other parameters and was controlled by the KS ratio between the different soils. The PFAS models in the homogeneous and heterogeneous scenarios were both sensitive to θS, depending on PFAS chain length. However, long-chain PFAS were less sensitive to θS variability compared to short-chain PFAS due to their higher Kd. The homogeneous and heterogeneous scenarios were equally sensitive to Kd variability, which was dependent on PFAS chain length. 

This study evaluates the performance of multiple machine learning (ML) algorithms and electrical resistivity (ER) arrays for inversion with comparison to a conventional Gauss-Newton numerical inversion method. Four different ML models and four arrays were used for the estimation of only six variables for locating and characterizing hypothetical subsurface targets. The combination of dipole-dipole with Multilayer Perceptron Neural Network (MLP-NN) had the highest accuracy. Evaluation showed that both MLP-NN and Gauss-Newton methods performed well for estimating the matrix resistivity while target resistivity accuracy was lower, and MLP-NN produced sharper contrast at target boundaries for the field and hypothetical data. Both methods exhibited comparable target characterization performance, whereas MLP-NN had increased accuracy compared to Gauss-Newton in prediction of target width and height, which was attributed to numerical smoothing present in the Gauss-Newton approach. MLP-NN was also applied to a field dataset acquired at U.S. DOE Hanford site. 

Physically based hydrologic models require significant effort and extensive information for development, calibration, and validation. The study explored the use of the random forest regression (RFR), a supervised machine learning (ML) model, as an alternative to the physically based Soil and Water Assessment Tool (SWAT) for predicting streamflow in the Rio Grande Headwaters near Del Norte, a snowmelt-dominated mountainous watershed of the Upper Rio Grande Basin. Remotely sensed data were used for the random forest machine learning analysis (RFML) and RStudio for data processing and synthesizing. The RFML model outperformed the SWAT model in accuracy and demonstrated its capability in predicting streamflow in this region. We implemented a customized approach to the RFR model to assess the model’s performance for three training periods, across 1991–2010, 1996–2010, and 2001–2010; the results indicated that the model’s accuracy improved with longer training periods, implying that the model trained on a more extended period is better able to capture the parameters’ variability and reproduce streamflow data more accurately. The variable importance (i.e., IncNodePurity) measure of the RFML model revealed that the snow depth and the minimum temperature were consistently the top two predictors across all training periods. The paper also evaluated how well the SWAT model performs in reproducing streamflow data of the watershed with a conventional approach. The SWAT model needed more time and data to set up and calibrate, delivering acceptable performance in annual mean streamflow simulation, with satisfactory index of agreement (d), coefficient of determination (R2), and percent bias (PBIAS) values, but monthly simulation warrants further exploration and model adjustments. The study recommends exploring snowmelt runoff hydrologic processes, dust-driven sublimation effects, and more detailed topographic input parameters to update the SWAT snowmelt routine for better monthly flow estimation. The results provide a critical analysis for enhancing streamflow prediction, which is valuable for further research and water resource management, including snowmelt-driven semi-arid regions. 

Abstract Mountain‐front recharge (MFR), or all inflow to a basin‐fill aquifer with its source in the mountain block, is an important component of recharge to basin‐fill aquifer systems. Distinguishing and quantifying the surface from subsurface components of MFR is necessary for water resource planning and management, particularly as climate change may impact these components in distinct ways. This study tests the hypothesis that MFR components can be distinguished in long‐screened, basin‐fill production wells by (1) groundwater age and (2) the median elevation of recharge. We developed an MFR characterization approach by combining age distributions in six wells using tritium, krypton‐85, argon‐39, and radiocarbon, and median recharge elevations from noble gas thermometry combined with numerical experiments to determine recharge temperature lapse rates using flow and energy transport modeling. We found that groundwater age distributions provided valuable information for characterizing the dominant flow system behavior captured by the basin‐fill production wells. Tracers indicated the presence of old (i.e., no detectable tritium) water in a well completed in weathered bedrock located close to the mountain front. Two production wells exhibited age distributions of binary mixing between modern and a small fraction of old water, whereas the remaining wells captured predominantly modern flow paths. Noble gas thermometry provided important complementary information to the age distributions; however, assuming constant recharge temperature lapse rates produced improbable recharge elevations. Numerical experiments suggest that surface MFR, if derived from snowmelt, can locally suppress water table temperatures in the basin‐fill aquifer, with implications for recharge elevations estimated from noble gas thermometry.