Groundwater最新文献

The Humboldt River, in Northern Nevada, USA, is a source of water for agricultural, municipal, and industrial uses. Mining has been active in Nevada since the 1800s. The recognition that groundwater withdrawal for many purposes—including mining—has the potential to deplete streamflow has prompted studies of the Humboldt River Basin. Nevada Gold Mines (NGM) operates several mine complexes in the Middle Humboldt River Basin. Although dewatering rates have reached about 3.9 cubic meters per second or greater (m³/s: 100,000 acre-feet per year [afy]), most water was not consumed by mine operations. Rather, most water was returned to groundwater, used to augment surface water, or used to offset other groundwater withdrawals. The objective of this study is to evaluate the effect of groundwater use and management by NGM on surface water in the Middle Humboldt River Basin, emphasizing the Humboldt River and spanning mine development through post-closure and pit lake formation. The study proceeds by first estimating historical and potential future net groundwater uses; then identifying features that control the propagation of pumping effects toward surface water bodies; and then using expedient calculations to estimate the locations, timing, and quantities of surface-water depletion. The study yields a best estimate and an ensemble of results illustrating a range of potential outcomes. As a result of water management practices and identified factors that delay and diminish stream depletion versus the mine dewatering, the study finds that the peak stream depletion is likely to be on the order of 0.25 m³/s (about 6000 afy), a rate which may be challenging to discern for much of the stream hydrograph.

Braided river systems are an important source for groundwater recharge, but their complex morphology makes river–groundwater exchange fluxes difficult to estimate. Their river channel morphology changes frequently after floods, which has effects on recharge rates that have rarely been studied in the past. This work aims to isolate the effects of changes in braided river morphology on groundwater recharge for two sections of the Wairau River and Waikirikiri River in New Zealand. For each study site, two different river morphology variants of a fully coupled surface water–groundwater model utilizing high-resolution DEMs of river bathymetry before and after a major flood event were set up while keeping parameterization and boundary conditions the same. The models demonstrate that flood-induced morphology changes in braided river systems alter groundwater recharge. We identify features, both simulated and observed, that explain the direction of change. Features that increase groundwater recharge are a larger braidplain aquifer extent and volume, larger wetted area and, specifically, an increase of areas with high exchange rates in locations of larger gradients between braidplain aquifer and regional aquifer. These factors influence groundwater recharge independent of connection (Wairau River) or disconnection (Waikirikiri River) of the system to the regional aquifer, albeit with different magnitudes. An extension of our research to other braided rivers is needed to more broadly generalize our findings.

A cross-site comparison of 49 springs from the United States and Europe with more than a 30-year record of spring discharge was conducted to look for response to climate change (temperature and precipitation). The dataset included both carbonate and non-carbonate springs across urban and rural settings, covering a wide range of discharge (0.001 to 25 m³/s) and discharge variability (45 to 6500% variability index) to encompass aquifers with a variety of hydrologic regimes and degrees of karstification. Relatively small changes in discharge trends (i.e., change in discharge over the long-term time record) and similar responses across the diverse spring dataset were observed, which suggested that groundwater storage buffers hydrologic stresses in these aquifers despite varying degrees of karstification. Although decreases in discharge were associated with an observed increase in air temperature, larger air temperature trends did not lead to larger discharge trends. Spring discharge trends over time could not be categorized by spring behavior as measured by mean discharge, variability in discharge, or rural vs. urban setting. The detection of trends was sensitive to the time period being considered, with selected time periods yielding different and sometimes opposite discharge trends, highlighting the importance of time period selection when making cross-site comparisons. This study benefited from cross-site comparison by finding similarities in spring discharge trends across varied karst regimes.

Groundwater occurrence in the Deccan basalt is highly uncertain. Non-invasive hydro-geophysical techniques, when integrated with a priori geological information, can aid in delineating saturated zones. This study uses electrical resistivity anisotropy with direct current (DC) resistivity and time-domain induced polarization (IP) for delineating groundwater prospects. We utilized the geophysical responses of DC resistivity, induced polarization, anisotropy and available borehole litholog data to locate the potential aquifers. Anisotropic inversion of DC resistivity data enables the identification of fractured basalt. We observed shallow saturation zones where fractured basalt overlies less permeable layers and deeper confined zones below the intertrappeans within fractured amygdaloidal basalts. The most productive shallow aquifers exhibit low to moderate resistivity (1 to 36 Ωm), and moderate to low chargeability (1.1 to 8 mV/V) and anisotropy (1.1 to 1.21). Finally, we conducted aquifer tests in different wells in the study area to quantify the aquifer parameters. This study offers a strong scientific basis for the assessment of groundwater potential zones and their management in the complex basaltic terrains of the Deccan province.

Groundwater plays an irreplaceable role in maintaining ecosystem stability and supporting production and daily life. Its extensive and long-term development and utilization have attracted increasing academic attention to groundwater research. In this study, we employed bibliometric analysis of 3514 graduate dissertations published between 1988 and 2024, extracted from the China National Knowledge Infrastructure (CNKI), examining their quantitative output, institutional affiliations, research fields, and keywords to explore groundwater research trends. The analysis indicates a growing focus on groundwater research, with 1066 (30%) graduate dissertations published in the past 5 years. North China, East China, and Northwest China were identified as the primary regions hosting groundwater research institutions. As a typical interdisciplinary field, “Earth Sciences,” “Industrial Technology,” and “Environmental Science” emerged as active intersecting disciplines. Moreover, keywords such as “numerical simulation,” “hydrochemical characteristics,” and “isotopes” were identified as high-frequency terms. Despite some progress, groundwater research continues to face challenges in several areas, including but not limited to the characterization of complex media, the integration of multi-source data, and the understanding of hydrogeochemical mechanisms. Based on keyword co-occurrence network and burst analysis, we predict future primary research directions. The integration of traditional disciplines such as hydrology, biology, and geochemistry is becoming increasingly prominent, while convergence with emerging fields like machine learning, big data, and artificial intelligence represents a frontier trend. The combined application of multiple technologies is becoming the optimal choice for addressing complex groundwater issues. In summary, this study provides a new perspective and offers broader insights for the research and development in the field of groundwater.

A large language model (LLM) coding agent can build complete MODFLOW 6 input file sets from natural-language descriptions when given the right reference material, without retraining the model. The reference material, which we call a plugin, comprises 42 plain-text package files (one per MODFLOW 6 package, written from the official Input/Output Guide, the Fortran source code, and the simulator's runtime error messages), plus seven workflow files and one configuration file. A suite of 59 pass-or-fail checks covered five tasks: steady-state flow, transient flow with pumping, coupled flow and solute transport, coupled flow and heat transport, and validation of an existing model. On the unmodified plugin, 52 of 59 checks passed (88%); the seven failures were all runtime errors, six of them caused by constraints spanning more than one input file. Two independent improvement strategies followed: an expert-guided batch correction and an automated one-failure-at-a-time loop. Both reached 59 of 59 (100%) and modified the same four skill files; the expert-guided strategy, which identified five root causes, made one additional edit to a workflow command file. This convergence indicates that the failures came from gaps in the reference files, not from random variation in the model's output. The automated loop required no human intervention. The methodology has been replicated on a sibling simulator (MODFLOW USG-Transport; Krčmář et al. 2026b) with 100% first-attempt success. The result is a reusable design pattern for adapting an LLM coding agent to a scientific simulator without retraining the model.

Groundwater is the principal source of freshwater in hyper-arid regions such as the Algerian Sahara, making reliable groundwater quality assessment essential for sustainable water resource management. This study developed an interpretable hybrid modeling framework for predicting the groundwater WQI in the Ghardaia region by integrating Particle Swarm Optimization (PSO) with ensemble machine learning algorithms. A total of 540 groundwater samples were collected from 67 artesian boreholes during the dry/wet seasons over the period 2021 to 2025. To ensure robust model evaluation and minimize data leakage, a spatially independent validation strategy. Based on borehole locations were adopted. Three ensemble algorithms, namely Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost), were optimized using PSO and trained separately for the dry/wet-season datasets. Among the evaluated models, PSO-AdaBoost achieved the best predictive performance during the dry season, with R² = 0.976, whereas PSO-XGBoost performed best during the wet season, with R² = 0.985. Hydrochemical variables' contribution to WQI predictions was quantified by investigating model interpretability using SHapley Additive exPlanations (SHAP). SHAP analysis revealed that potassium (K⁺) was the most influential predictor during the dry season, reflecting strong evapoconcentration and water–rock interaction processes. pH and magnesium (Mg²⁺) became relatively more influential during wet-period sampling campaigns, suggesting temporal hydrochemical variability potentially associated with physicochemical equilibrium changes and indirect hydrodynamic influences. The proposed PSO-ensemble-SHAP framework provides an accurate and interpretable approach for groundwater quality prediction and supports efficient monitoring and management of groundwater resources in arid aquifer systems.