Groundwater最新文献

The concentration of fluoride (F⁻) in many geothermal waters worldwide exceeds the World Health Organization's drinking water guideline of 1.5 mg/L, highlighting a widespread water quality issue in geothermal systems. It is essential to predict the mobility of F⁻ in geothermal reservoirs compared to that of chloride (Cl⁻). Column experiments were conducted at 45°C under hydrostatic pressures from atmospheric pressure to 12 MPa to simulate geothermal reservoir conditions, with the computer code CXTFIT 2.1 used to fit the data and determine transport and adsorption parameters of Cl⁻ and F⁻. The effect of hydrostatic pressure on F⁻ transport was evaluated. Results showed that the Convection–Dispersion Equation (CDE) accurately fits the breakthrough curves of Cl⁻ and higher pressures enhance its dispersion transport. In contrast, F⁻ transport under atmospheric pressure shows rate-limited non-equilibrium, with the Two-Site Model (TSM) fitting F⁻ curves better. However, when pressure exceeds 6 MPa, equilibrium adsorption becomes more pronounced, and the equilibrium CDE better characterizes F⁻ transport. Higher pressure also increases the retardation factor (R) of F⁻, suggesting more instantaneous adsorption sites under these conditions. The release of hydroxide (OH⁻) during F⁻ adsorption increases pH values in the effluent notably. Moreover, greater pH fluctuations occur with increasing pressure due to the enhanced adsorption of F⁻ by the packed matrix. The findings would be helpful for understanding the solute transport and adsorption mechanism of fluoride under varying hydrostatic pressures in groundwater and geothermal systems.

The tracer flowmeter and depth-dependent sampler (TFDDS) has been used to characterize flow and chemistry in hundreds of public supply wells. The TFDDS surveys address production and water quality compliance issues for utilities facing mounting treatment costs. Surveyed wells often show a stratified distribution for constituents of concern that were underestimated and poorly discretized using conventional zone testing in the pilot hole. Elevated concentrations of metals, semimetals, and radionuclides typically occurring at or near lithologic boundaries between fine- and coarse-grained sediments are missed since the primary focus of the zone test is to estimate the yield of the permeable sediments that are centered within a coarse unit. To minimize the risk of constructing new wells that fail to produce compliant drinking water relative to regulatory standards, we propose a new approach that vertically stacks flow and mass balance chemistry profiling of long screened test wells (LSTWs) under pumping conditions prior to installing the more costly public supply well. This approach integrates the TFDDS with a spectrum of drilling methods to rapidly provide hydraulic and chemistry data. A detailed distribution of anthropogenic and geogenic constituents within the saturated zones is produced, characterizing both permeable sediments and contaminant-laden boundaries between finer- and coarser-grained deposits. Applying this high-resolution data to well design leads to informed predictions on whether the new well will be compliant. Two case studies are presented, one for public supply well design and the other for managed aquifer recharge.

Increasing water demands and declining groundwater levels have led to rising interest in managed aquifer recharge. That interest is growing in the United States—the focus of this article—and elsewhere. Increasing interest makes sense; managed aquifer recharge can reduce water-supply challenges and provide environmental benefits, sometimes with lower costs than alternative water-management approaches. But managed aquifer recharge also faces growing pains, which will make it difficult for projects to scale up and may limit the benefits provided by those projects that do go forward. Some of the problems arise from the challenges of finding physically suitable locations for managed aquifer recharge; many derive from economics, public policy, and law; and some derive from ways in which managed aquifer recharge could exacerbate traditional equity challenges of water management. But as we explain, there also are potential solutions to these challenges, and the future success of managed aquifer recharge will likely depend on the extent to which these solutions are adopted.

Groundwater models are important and useful tools for answering scientific and technical questions about the quantity and quality of groundwater, as well as for making critical management decisions. However, the heterogeneity of subsurface properties, such as hydraulic conductivity, is known to play a central role in groundwater flow and transport; therefore, its accurate quantification and incorporation into the groundwater workflow are critical. This paper presents a novel tool, ArchPy2Modflow, that efficiently combines a stochastic geological generator, ArchPy, with a groundwater flow software, MODFLOW. ArchPy2Modflow provides a rapid and practical way to convert and link any ArchPy model to a new (or existing) MODFLOW model, where any MODFLOW spatial parameter (such as porosity, hydraulic conductivity, or storativity) can be obtained from an ArchPy property, which is then upscaled according to the MODFLOW grid. ArchPy2Modflow offers several different options for selecting the appropriate MODFLOW grid: using the same grid as in the ArchPy model, defining each ArchPy geological unit as a MODFLOW layer, coarsening the grid by a certain factor, or directly using an existing MODFLOW grid. This flexibility enables users to adapt their models to suit their needs and constraints. The usefulness and practicality of the new tool are demonstrated by a synthetic example considering flow and transport in a heterogeneous aquifer, while the impact of a particular grid selection on the simulations is demonstrated.

Adjoint sensitivity analysis provides an efficient alternative to direct methods when evaluating the influence of many uncertain parameters on a limited number of performance measures in hydrologic and hydrogeologic models. However, most adjoint implementations are “intrusive”, requiring extensive modifications of the forward simulation code. This creates significant development and maintenance burdens that limit broad adoption. To address these needs, we present MF6-ADJ, a “non-intrusive” adjoint sensitivity capability for the MODFLOW 6 groundwater flow process that leverages the MODFLOW Application Programming Interface (API) to interact with the forward groundwater flow solution without altering its core code. MF6-ADJ supports both confined and unconfined flow conditions, structured and unstructured grids, and is compatible with both the Standard and Newton–Raphson solution schemes. It computes sensitivities of a wide range of general performance measures, including hydraulic heads, boundary fluxes, and weighted residuals, with respect to key model parameters such as hydraulic conductivity, storage coefficient, injection/extraction rate, recharge rate, boundary head, and boundary conductance. Sensitivities are computed at each node, enabling fine-grained diagnostic and calibration analysis. Validation against analytical solutions and the finite-difference perturbation method confirms excellent agreement, while demonstrating speedups ranging from hundreds to tens of thousands of times depending on grid discretization, since the adjoint state method computes sensitivities efficiently at the grid-block level. This non-intrusive design makes MF6-ADJ highly accessible and maintainable, offering efficient and scalable sensitivity analysis in complex groundwater modeling workflows.

Groundwater quality changes in wells and streams lag behind changes to land use due to groundwater travel times. Two contaminant transport methods were compared to assess differences in their simulated travel time distributions (TTDs) to streams and wells in the Wisconsin Central Sands. MODPATH simulates advective groundwater flow with particle tracking, while MT3D simulates age-mass using a finite difference solution without dispersion to allow for direct comparison of the two methods. MODPATH appropriately simulates groundwater TTDs from the water table to surface discharge but is subject to inaccuracies at weak-sink well cells due to the flow-model grid discretization and imprecise location of well discharge within well cells. MT3D better represents weak-sink well cells since it removes mass in proportion to the prescribed pumping rate, although travel time within well cells is neglected. Conversely, MT3D's treatment of surface water boundary cells is not as accurate as MODPATH because mass should be removed from the water table rather than the full cell volume. MT3D simulations of TTDs can also be confounded by the instantaneous vertical distribution of mass introduced throughout recharge cells instead of at the water table, which initiates mass along deeper flow paths. We evaluated 9 MODPATH and 13 MT3D implementations, generating differences in median travel times of up to 18 years. Both methods have strengths and weaknesses, with MT3D better representing weak-sink well cell behavior and MODPATH better representing surficial recharge and discharge. The effect of these characteristics on simulated TTDs, along with ideas for ameliorating method weaknesses, is discussed.

Coastal lowlands are increasingly vulnerable to threats from sea-level and associated groundwater rise. This study introduces a categorical modeling framework that redefines groundwater depth estimation as a classification problem rather than a continuous prediction task. By dividing groundwater occurrence into multiple depth thresholds (0.9–2.0 m), the approach explicitly quantifies prediction uncertainty through Type I (false positive) and Type II (false negative) errors. A national-scale ensemble model developed at 100 m resolution using the Random Forest algorithm was trained on New Zealand's comprehensive depth-to-water database. Thirty-seven predictor variables, derived via PCA (97.5% variance retained) from 199 base predictors, were incorporated to capture the complex interactions influencing groundwater depth. The model demonstrates strong performance, with ROC–AUC values ranging from 0.823 to 0.962, and accuracy improves with increasing depth. This categorical framework addresses challenges associated with data imbalance and enhances uncertainty quantification compared to traditional regression methods. Probabilistic predictions allow stakeholders to set customizable risk thresholds and manage acceptable error levels based on specific coastal management contexts. By bridging the gap between advanced numerical modeling and practical adaptation planning, the approach provides a robust tool for evidence-based decision making in the face of rising sea levels.