Groundwater最新文献

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.

Land subsidence is widely present across the globe and brings catastrophic hazards. The well-acknowledged mechanism of subsidence is groundwater pumping, which leads to the reduction of hydraulic head and hence increases the effective stress, resulting in the vertical compaction of unconsolidated sediment. Here, we propose a hypothesis that subsidence in the coastal areas might be caused by osmotic effects, given the presence of seawater intrusion. The hypothesis is corroborated by simulating fluid flow, solute transport, and elastic deformation of multi-layered aquifer-aquitard systems. The simulations potentially cover a variety of natural environments by varying concentration, hydraulic head, thickness of aquitard, and hydraulic conductivity. We find that osmotic effects due to seawater intrusion play a non-negligible role in controlling subsidence in our studied cases, suggesting that future work on subsidence in areas impacted by seawater intrusion should fully incorporate osmotic effects to improve our understanding and prediction of subsidence.

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.

We propose that a new term, aquitardifer, be added to the hydrogeologic nomenclature. Aquitardifer, a blend of the terms aquitard and aquifer, accounts for geologic materials that have properties of both as traditionally defined. Several examples of aquitardifers are provided, as is justification for and applicability of the term.

Borehole nuclear magnetic resonance (NMR) can be used to estimate the hydraulic conductivity (K) of unconsolidated materials. Various petrophysical models have been developed to predict K based on NMR response, with considerable efforts on optimizing site-specific constants. In this study, we assessed the utility of NMR logs to estimate K within highly heterogeneous glaciofluvial deposits by comparing them with K measurements from three types of co-located hydraulic testing methods, including permeameter, multi-level slug, and direct-push hydraulic profiling tool (HPT) logging tests. Four NMR models, including Schlumberger-Doll Research (SDR), Seevers, Sum-of-Echoes (SOE), and Kozeny-Godefroy (KGM), were applied to construct K profiles at four locations with model constants optimized using permeameter-based K. Model constants suitable for glaciofluvial deposits were provided. Results showed that NMR logging can provide reliable K estimates for interbedded layers of sand/gravel, silt, and clay. Through cross-hole comparison of NMR-derived K profiles, the trends and magnitudes of K for aquifers/aquitards were readily mapped. Quantitatively, the NMR-derived K coincided with hydraulic-testing K, with optimal model fits within one order of magnitude. We noticed that (1) Seevers performed similarly but no better than SDR in predicting permeameter and slug testing measurements; (2) SOE yielded slightly better predictions than SDR; (3) the removal of porosity in SDR did not deteriorate its prediction, and the optimized SDR constant resembled the literature-based values for glacial deposits; and (4) KGM yielded the optimal fits with slug-based K, demonstrating its reliable performance. Lastly, we made recommendations on selecting suitable petrophysical models.