Groundwater Transport Research Articles

Computational fluid dynamics simulation of rough bed open channels using openFOAM

With increased flood risk due to climate change, population expansion and urbanisation; robust waterway design and management are critical. One common type of waterway used to gather and transport ground water is the open channel. Most simulations do not account for the physical roughness of the bed, instead using a roughness coefficient. This means that only the turbulent energy content can be modelled whilst physical turbulent eddies and vortices cannot. Furthermore, many past studies assume the free surface is a rigid lid. This could affect the way that turbulent structures near the free surface behave. Computational Fluid Dynamics simulation of an open channel with a rough bed and rigid lid are conducted using OpenFOAM. Results show good correlation with experimental tests. It can be visually observed that turbulent structures generated from the rough bed do interact with the free surface and thus a rigid lid is perhaps not a great approximation. This is supported by an apparent decrease in the Reynolds shear stress from the free surface and 30% of the flow depth immediately beneath.

Using sequential conditioning to explore uncertainties in geostatistical characterization and in groundwater transport predictions

Rapid transmission of contaminants in groundwater can occur in alluvial gravel aquifers that are permeated by highly conductive small-scale open framework gravels (OFGs). This open framework gravel structure and the associated distribution of hydraulic properties is complex, and so assessments of contamination risks in these aquifers are highly uncertain. Geostatistical models, based on lithological data, can be used to quantitatively characterize this structure. These models can then be used to support analyses of the risks of contamination in groundwater systems. However, these geostatistical models are themselves accompanied by significant uncertainty. This is seldom considered when assessing risks to groundwater systems. Geostatistical model uncertainty can be reduced by assimilating information from hydraulic system response data, but this process can be computationally challenging. We developed a sequential conditioning method designed to address these challenges. This method is demonstrated on a transition probability based geostatistical simulation model (TP), which has been shown to be superior for representing the connectivity of high permeability pathways, such as OFGs. The results demonstrate that the common modelling practice of adopting a single geostatistical model may result in realistic predictions being overlooked, and significantly underestimate the uncertainties of groundwater transport predictions. This has important repercussions for uncertainty quantification in general. It also has repercussions if using ensemble-based methods for history matching, since it also relies on geostatistical models to generate prior parameter distributions. This work highlights the need to explore the uncertainty of geostatistical models in the context of the predictions being made.

Preferential Retention and Transport of Perfluorooctanesulfonic Acid in a Dolomite Aquifer.

Per- and polyfluoroalkyl substances (PFAS) can represent a significant human health risk if present in aquifers used as a drinking water source. Accurate assessment of PFAS exposure risks requires an improved understanding of field-scale PFAS transport in groundwater. Activities at a former firefighter training site in University Park, Pennsylvania introduced perfluorooctanesulfonic acid (PFOS) to the underlying dolomite aquifer. Groundwater sampling from 2015 to 2018 delineated a PFOS plume with two concentration maxima located approximately 20 and approximately 220 m downgradient of the training site, separated by a zone of lower concentrations. We use a combination of analytical and numerical models, informed by independent measurements of aquifer porosity, hydraulic conductivity, and organic carbon content, to interpret the field observations. Our analysis demonstrates that preferential retention and transport resulting from simple heterogeneity in bedrock sorption, as caused by organic carbon (OC) content variability, provides a plausible explanation for plume separation. Dissolved PFOS partitions strongly to organic solids (high Koc ), so even a small OC (<1wt%) significantly retards PFOS transport, whereas zones with little to no OC allow for transport rates that approximate those of a conservative solute. Our work highlights an important consideration for modeling the groundwater transport of PFOS, and other compounds with high Koc . In aquifers with discrete layers of varying OC, models using a uniform site-average OC will underestimate transport distances, thereby misrepresenting exposure risks for downgradient communities.

Coupled flow and transport simulation involving rate-limited adsorption in highly heterogeneous unconfined aquifers using a local strong form meshless method

Adsorption plays a vital role in contaminant transport in groundwater. Generally, a local equilibrium assumption is used in which the retardation factor is derived from aquifer material and contaminant type. However, for problems involving faster seepage velocities, this assumption becomes invalid and the mass transfer between mobile and immobile or, sorbed phases of contaminant becomes rate-limited. Traditionally, finite difference method (FDM) models are employed for contaminant transport simulation with rate-limited adsorption (RLA). Nonetheless, due to the presence of a grid, these models show instabilities, especially for advection dominant problems. In the present study, a meshless Radial Point Collocation Method (RPCM-RLA) is proposed for contaminant transport simulation with rate-limited adsorption. The proposed model is verified with semi-analytical solutions and its efficacy is assessed with the popularly used FDM-based Reactive Transport in 3 Dimensions (RT3D) model. The RPCM-RLA model is observed to provide stable and accurate solutions even for advection dominant problems whereas RT3D model simulations show excessive numerical dispersion. Furthermore, the effectiveness of RPCM-RLA model is demonstrated in two case studies concerning heterogeneous unconfined aquifers by comparing it with RT3D. Both model simulations agree consistently with maximum values of normalized mean absolute error (NMAE) being 0.0055 and 0.031 for case studies 1 and 2 respectively, with better performance of RPCM-RLA model.

A Lagrangian particle model on GPU for contaminant transport in groundwater

A Lagrangian particle model on GPU for contaminant transport in groundwater

Digital transformation in rock physics: Deep learning and data fusion

Rock physics plays an essential role in geophysical reservoir characterization. It aims to build a bridge between geophysical measurements and in-situ rock and fluid properties. With the advancement of microscopic imaging and computer science, rock physics is transitioning to the digital age. This is referred to as digital rock physics (DRP). DRP provides a nondestructive and efficient way to determine physical rock properties directly from digital images. Over the last decades, it has become a routine tool in reservoir characterization by complementing or replacing expensive and time-consuming laboratory measurements. With the emergence of deep learning, DRP has advanced significantly from image processing to physical simulation. This paper presents an application of deep learning in multiscale fusion of digital rock images. It aims to overcome the trade-off between image resolution and field of view (FoV) by integrating imaging data from multiple sources including (1) 3D microcomputed tomography images at micronscale with a large FoV and (2) 2D scanning electron microscopy images at nanoscale with a small FoV. The reconstructed image integrates information of microstructures at different scales and helps characterize heterogeneous porous rocks more accurately. It is helpful to improve the prediction accuracy of effective rock properties and to have deeper insight into physical processes at pore scale. Data fusion based on deep learning would unlock new pathways for geophysical characterization of porous rocks, with broad implications for various subsurface applications such as groundwater transport, enhanced oil recovery, and geologic carbon sequestration.

Particle tracking as a vulnerability assessment tool for drinking water production

The simulation of concentration values and use of such data for history-matching is often impeded by the computation time of groundwater transport models based on the resolution of the advection-dispersion equation. This is unfortunate because such data are often rich in information and the prediction of concentration values is of great interest for decision making. Particle tracking can be used as an efficient alternative under a series of simplifying assumptions, which are often reasonable at groundwater sinks (wells and drains). Our approach consists of seeding particles around a sink and tracking particles backward, up to the source boundary condition, such as a contaminated stream. This particle tracking approach allows the use of parameter estimation and optimization methods requiring numerous model calls. We present a Python module facilitating the pre- and post-processing operations of a modeling workflow based on the widely used USGS MODFLOW6 and MODPATH7 programs. The module handles particle seeding around the sink and estimation of the mixing ratio of water withdrawn from the sink. This ratio is computed with a mixing law from the particle endpoints, accounting for particle velocities and mixing in the source model cells. We investigate the best practice to obtain robust derivatives with this approach, which is a benefit for the screening methods based on linear analysis. We illustrate the interest of the approach with a real world case study, considering a drinking water well field vulnerable to a contaminated stream. The configuration is typical of many other drinking water production sites. The modeling workflow is fully script-based to make the approach easily reproducible in similar cases.

Characteristics of the Rongcheng Bulge Geothermal Field and the Evolution of Geothermal Fluids, Xiong’an New Area, China

The study of geothermal field characteristics and the mechanisms involved in the hydrogeochemical evolution of geothermal fluids is an effective means to understand the formation, reservoir conditions and circulation mechanics of geothermal resources. Documenting these parameters within the Rongcheng Bulge of Xiong’an New Area, China, is of great significance to its future development and utilization as a geothermal resource. In this paper, we investigate the hydrochemical characteristics of geothermal fluids and the rock thermal properties of the Rongcheng Bulge Reservoir and the surrounding rocks to identify the mechanisms of geothermal fluid genesis within the geothermal field. The results show that the geothermal water in the Rongcheng Bulge is mainly recharged by atmospheric rainfall. The thermal storage temperature at which the deep geothermal fluid is located ranges between 63 and 105 °C, whereas the depth of geothermal water circulation is between 1500 and 2700 m. Fluid exchange is weak during deep circulation, but as the geothermal fluid rises, the proportion of cold water mixed with the geothermal waters is 75–95%. The overall geothermal gradient of the carbonate thermal reservoir is significantly lower than that within the overburden and basement rocks, probably due to convection during the upward transport of groundwater in the reservoir. The geothermal fluid in the area of the Rongcheng Bulge is recharged by the Taihang Mountains and atmospheric precipitation. Following recharge and deep circulation, the fluids rise along fractures and by interlayer convection and are mixed with cold water to form the current, accessible geothermal water.

Hydrogeological multiple-point statistics inversion by adaptive sequential Monte Carlo

For strongly non-linear and high-dimensional inverse problems, Markov chain Monte Carlo (MCMC) methods may fail to properly explore the posterior probability density function (PDF) given a realistic computational budget and are generally poorly amenable to parallelization. Particle methods approximate the posterior PDF using the states and weights of a population of evolving particles and they are very well suited to parallelization. We focus on adaptive sequential Monte Carlo (ASMC), an extension of annealed importance sampling (AIS). In AIS and ASMC, importance sampling is performed over a sequence of intermediate distributions, known as power posteriors, linking the prior to the posterior PDF. The AIS and ASMC algorithms also provide estimates of the evidence (marginal likelihood) as needed for Bayesian model selection, at basically no additional cost. ASMC performs better than AIS as it adaptively tunes the tempering schedule and performs resampling of particles when the variance of the particle weights becomes too large. We consider a challenging synthetic groundwater transport inverse problem with a categorical channelized 2D hydraulic conductivity field defined such that the posterior facies distribution includes two distinct modes. The model proposals are obtained by iteratively re-simulating a fraction of the current model using conditional multiple-point statistics (MPS) simulations. We examine how ASMC explores the posterior PDF and compare with results obtained with parallel tempering (PT), a state-of-the-art MCMC inversion approach that runs multiple interacting chains targeting different power posteriors. For a similar computational budget, ASMC outperforms PT as the ASMC-derived models fit the data better and recover the reference likelihood. Moreover, we show that ASMC partly retrieves both posterior modes, while none of them is recovered by PT. Lastly, we demonstrate how the power posteriors obtained by ASMC can be used to assess the influence of the assumed data errors on the posterior means and variances, as well as on the evidence. We suggest that ASMC can advantageously replace MCMC for solving many challenging inverse problems arising in the field of water resources.

Graphical Shading Logs: An Improved Approach for Collecting High Resolution Sedimentological Data at Contaminated Sites

AbstractPredicting contaminant transport in groundwater requires an accurate representation of the subsurface geology controlling the spatial distribution of hydrogeologic parameters. Developing accurate geological models for sedimentary systems relies on quality sedimentological data collected from cores. Standard logging forms used to collect data from cores create a persistent data gap in hydrogeology because they hinder efficient collection of high‐quality sedimentological data. These logging forms require time‐consuming text descriptions of sedimentological characteristics and often result in inconsistent, poorly resolved data insufficient to support realistic geological models. We describe a graphical approach to core logging, the graphical shading log, that facilitates rapid, accurate capture of sedimentological data and a complementary database to store the raw data and interpretations. The visual format of the graphical shading log provides a roadmap of the parameters to log and their possible values, helping to ensure accurate and consistent data collection by loggers with a range of experience. Examples from sites with contaminated groundwater in glaciogenic sediments and siliciclastic and carbonate bedrock show how data from the graphical shading logs improved geological interpretations, supported the design of high‐resolution multilevel systems needed to collect minimally blended hydrogeologic data, and helped to more accurately delineate hydrogeologic units. The format of the graphical shading log and complementary database are designed to be customizable and transferable between hydrogeologic settings providing a new tool to advance geological data collection and management. Improved sedimentological data and insight are critical inputs for process‐based conceptual site models needed to effectively manage contaminant plumes in the subsurface.

Regional patterns and drivers of total nitrogen trends in the Chesapeake Bay watershed: Insights from machine learning approaches and management implications

Anthropogenic nutrient inputs have led to nutrient enrichment in many waterbodies worldwide, including Chesapeake Bay (USA). River water quality integrates the spatial and temporal changes of watersheds and forms the foundation for disentangling the effects of anthropogenic inputs. We demonstrate with the Chesapeake Bay Non-Tidal Monitoring Network that machine learning approaches – i.e., hierarchical clustering and random forest (RF) classification – can be combined to better understand the regional patterns and drivers of total nitrogen (TN) trends in large monitoring networks, resulting in information useful for watershed management. Cluster analysis revealed regional patterns of short-term TN trends (2007-2018) and categorized the stations into three distinct trend clusters, namely, V-shape (n = 23), monotonic decline (n = 35), and monotonic increase (n = 26). RF models identified regional drivers of TN trend clusters by quantifying the effects of watershed characteristics (land use, geology, physiography) and major N sources on the trend clusters. Results provide encouraging evidence that improved agricultural nutrient management has resulted in declines in agricultural nonpoint sources, which in turn contributed to water-quality improvement in our period of analysis. Moreover, water-quality improvements are more likely in watersheds underlain by carbonate rocks, reflecting the relatively quick groundwater transport of this terrain. By contrast, water-quality improvements are less likely in Coastal Plain watersheds, reflecting the effect of legacy N in groundwater. Notably, results show degrading trends in forested watersheds, suggesting new and/or remobilized sources that may compromise management efforts. Finally, the developed RF models were used to predict TN trend clusters for the entire Chesapeake Bay watershed at the fine scale of river segments (n = 979), providing fine spatial information that can facilitate targeted watershed management, including unmonitored areas. More broadly, this combined use of clustering and classification approaches can be applied to other regional monitoring networks to address similar water-quality questions.

Demonstration of Managed Aquifer Recharge in a Coastal Plain Aquifer: Lessons Learned

The sustainable water initiative for tomorrow (SWIFT) is a 378,000 m3 /day (100 MGD) managed aquifer recharge (MAR) program designed by the Hampton Roads Sanitation District (HRSD) to rehabilitate the Potomac Aquifer System (PAS) in the Coastal Plain of Eastern Virginia. Groundwater is a primary water source in Eastern Virginia with over 93% of reported use derived from the PAS. Starting in May 2018, HRSD has operated a 3780 m3 /day (1.0MGD) MAR demonstration facility at the SWIFT Research Center (SWIFT-RC) in Suffolk, Virginia. The primary aim of the SWIFT-RC is to demonstrate, at a meaningful scale, the feasibility of MAR using deep well recharge into confined PAS hydrostratigraphic unit. The SWIFT-RC employs advanced water treatment technology to bring secondary effluent from an HRSD wastewater treatment plant to drinking water standards. Lessons learned include the evaluation and selection of a multiple barrier carbon-based treatment system to ensure water quality and maintain geochemical compatibility between MAR water and native groundwater, and the evaluation and selection of aluminum chlorohydrate for stabilizing aquifer clays immediately around the well to accept the fresher recharge water. The distribution of flow in the SWIFT-RC multiscreen recharge well and associated well injectivity were variable with time resulting from changing conditions in the well. Dynamic recharge well performance was quantified through the combined analysis of intrinsic and artificial tracer transport, in situ flowmeter testing, and water level analysis. Monitoring well nests and a depth-discrete sampling system supported a robust sampling plan to analyze chemical transport and attenuation in SWIFT-RC groundwater.

A hybrid approach based on simulation, optimization, and estimation of conjunctive use of surface water and groundwater resources.

Due to limited groundwater resources in arid and semi-arid areas, conjunctive use of surface water and groundwater is becoming increasingly important. In view of this, there are needs to improve the methods for conjunctive use of surface and groundwater. Using numerical models, optimization algorithms, and machine learning, we created a new comprehensive methodological structure for optimal allocation of surface and groundwater resources and optimal extraction of groundwater. The surface and groundwater system was simulated by MODFLOW to reflect groundwater transport and aquifer conditions. The important Marvdasht aquifer in the south of Iran was used as an experimental study area to test the methodology. In this context, we developed an optimal conjunctive exploitation model for dry and wet years using two new evolutionary algorithms, i.e., whale optimization algorithm (WOA) and firefly algorithm (FA). These were used in combination with the group method of data handling (GMDH) and least squares support vector machine (LS-SVM) to estimate sustainable groundwater withdrawal. The results show that the FA is more efficient in calculating optimal conjunctive water supply so that about 61% of water needs were met in the worst scenario for surface water resources, while it was 52% using the WOA. By applying the optimal conjunctive model during the simulation period, the groundwater level increased by about 0.4 and 0.55m using the WOA and FA, respectively. The results of Taylor's diagram, box plot diagram, and rock diagram with error evaluation criteria, i.e., root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE), showed that the GMDH (RMSE = 6.04 MCM, MAE = 3.89 MCM, and NSE = 0.99) was slightly better than LS-SVM (RMSE = 6.36 MCM, MAE = 4.50 MCM, and NSE = 0.98) to estimate optimal groundwater use. The results show that machine learning models are cost- and time-effective solutions to estimate optimal exploitation of groundwater resources in complex combined surface and groundwater supply problems. The methodology can be used to better estimate sustainable exploitation of groundwater resources by water resources managers.

Analytical modeling of contaminant transport in groundwater using the Karanovic solution: a case study of Baruwa, Nigeria

Analytical modeling of contaminant transport in groundwater using the Karanovic solution: a case study of Baruwa, Nigeria

Modeling a well-characterized perfluorooctane sulfonate (PFOS) source and plume using the REMChlor-MD model to account for matrix diffusion

Two of the most important retention processes for per- and polyfluoroalkyl substances (PFAS) in groundwater likely are sorption and matrix diffusion. The objective of this study was to model concentration and mass discharge of one PFAS, perfluorooctane sulfonate (PFOS), with matrix diffusion processes incorporated using data from a highly chemically- and geologically-characterized site. When matrix diffusion is incorporated into the REMChlor-MD model for PFOS at this research site, it easily reproduces the field data for three key metrics (concentration, mass discharge, and total mass). However, the no-matrix diffusion model produced a much poorer match. Additionally, after about 40 years of groundwater transport, field data and the REMChlor-MD model both showed the majority (80%) of the measured PFOS mass that exited the source zones was located in downgradient low permeability zones due to matrix diffusion. As such, most of the PFOS mass is not available to immediately migrate downgradient via advection in the more permeable sands at this site, which has important implications for monitored natural attenuation (MNA). Plume expansion over the next 50 years is forecasted to be limited, from a 350-m plume length in 2017 to 550 m in 2070, as matrix diffusion will attenuate groundwater plumes by slowing their expansion. This phenomenon is important for constituents that do not degrade, such as PFOS, compared to those susceptible to degradation. Overall, this work shows that matrix diffusion is a relevant process in environmental PFAS persistence and slows the rate of plume expansion over time.

Numerical simulation of benzene transport in shoreline groundwater affected by tides under different conditions

Numerical simulation of benzene transport in shoreline groundwater affected by tides under different conditions

Reactive transport modeling of U and Ra mobility in roll-front uranium deposits: Parameters influencing 226Ra/238U disequilibria

Uranium reserve estimates in ore deposits can be significantly impacted by 226Ra/238U disequilibria arising from the differential mobility of uranium and radium during groundwater transport. 1D reactive transport models were developed to investigate the long-term effects of retention processes (UO2(am) precipitation, U(VI) and Ra sorption on smectite, Ra co-precipitation with barite) on the repartitioning of 238U and 226Ra during formation of roll-front type deposits. Analytical solutions to radioactive decay chains were used in complement to examine the influence of geochemical parameters, including fluid 234U/238U activity ratios and α-recoil loss, on 226Ra/238U disequilibria in uranium ores. Model results demonstrate that smectite and barite can produce 226Ra/238U ratios >1 at low uranium contents and may explain 226Ra/238U disequilibria occurring in altered rock up- and downstream of roll-front deposits. The capacity of these phases to take up Ra and generate 226Ra/238U disequilibria depends on both mineral contents and groundwater compositions, and is thus expected to be site-specific. Simulations of ore deposits that advance downstream with time demonstrate the formation of stronger 226Ra/238U disequilibria, as expected, in the downgradient side or nose of the ore, reflecting both younger mineralization ages and the presence of active uranium precipitation. Whether disequilibria are positive or negative with respect to secular equilibrium, however, depends on the 234U/238U activity ratio in the fluid from which uranium minerals precipitate. Smaller hydraulic conductivities are shown to generate a narrower range in 226Ra/238U activity ratios with distance, and may explain the occurrence of disequilibria in the limb ore that are less pronounced than those in the nose. Furthermore, the ability of α-recoil loss to decrease 226Ra/238U activity ratios at secular equilibrium may account for negative disequilibria in high grade ores. The South Tortkuduk uranium deposits (Kazakhstan) are subsequently used as a case study to identify the processes and parameters that may contribute to 226Ra/238U disequilibria at this site. Variations in multiple parameters, including clay contents, barite contents, and mineralization ages, are found to reproduce measured 226Ra/238U activity ratios in the roll-front ore. Prioritization of these parameters will necessitate field measurements targeting both groundwater fluids and the host rock. Results from this study will ultimately aid geologists in building appropriate hydrogeochemical data sets to more efficiently locate and exploit uranium ore deposits.

New Vision for Water Safety Plan Experimental study of Deformable Porous Media for Aquifer storage and Recovery in Jordan

Transportation in porous media is a very interested topic, which has attracted many researchers from various disciplines especially for water resources. This research covers materials related to details related to the study of groundwater transport across layers. The research results are used to understand and define the problems of porous media and to produce knowledge of the theoretical and practical aspects of transporting porous media for different purposes of water and gases, including carrying out laboratory and actual volume tests, interpreting the model with the help of test and then predicting and simulating various porous media processes under different pressures. Seismic tests are conducted to find out the soil layer under the examination area and how water and gases are transferred. The work study to the investigate the critical factors in the design and implementation of the Underground Storage and Recovery (ASR) program? To reduce evaporation from exposed dams. And to analyze the water footprint in Jordan and Reducing the artificial ground subterranean projects in Jordan. Aesthetic plastic surgery in Jordan.

A comparative study of the sorption of O-PAHs and PAHs onto soils to understand their transport in soils and groundwater

Polycyclic aromatic hydrocarbons (PAHs) and their oxygenated derivates (O-PAHs) are identified in soils and groundwater of industrialized sites and contribute to the risk for Humans and the Environment. Nevertheless, data are scarce in literature concerning their retention and transfer in soils and no soil - water partition coefficients are available for these compounds. Sorption of two PAHs, fluorene and acenaphthene and two O-PAHs, 9H-fluorenone and dibenzofuran onto two soils with different organic carbon contents was evaluated and compared by determining their sorption isotherms. Effect of ionic strength and liquid to solid ratio, on fluorene and fluorenone sorption was also evaluated. Sorption equilibrium is achieved within less than 24 hr of mixing and linear sorption models best fit the isotherm data. Acenaphthene and dibenzofuran are similarly sorbed onto the soil. KD of fluorene is higher than the one of fluorenone, showing a smaller affinity of fluorenone towards the solid phase. This means that O-PAH could form larger contamination plumes in groundwater than PAHs. Decreasing the L/S ratio from 100 to 50 and 30, increases the sorption of fluorenone onto the soil by 56% and 67% respectively, while the sorption of fluorene is slightly increased. Increasing the ionic strength of the aqueous phase also modifies the sorption of fluorenone, contrary to the sorption of fluorene which is slightly affected.

Copper migration and isotope fractionation in a typical paddy soil profile of the Yangtze Delta

To decipher Cu migration in paddy soils, which is important for understanding Cu supply in rice cultivation, Cu concentrations and isotope compositions were measured in a paddy soil profile in Suzhou, Eastern China, in the central Yangtze Delta. The results show that the variations in δ65Cu values and Cu concentrations are not coupled along the profile. From top to bottom, the δ65Cu values show small variations (0.07 ± 0.03‰ to 0.25 ± 0.01‰) in the upper layers (Ap-Br1), with a decrease in the subsurface Br2 layer (from 0.16 ± 0.04‰ to −0.19 ± 0.02‰), are almost homogeneous in the transitional Br3-BCrg layers (−0.01 ± 0.01‰ to −0.10 ± 0.02‰), and further decrease to −0.33 ± 0.01‰ in the permanently submerged G1 and G2 layers. Copper concentrations in the Ap layer show some fluctuations (25.8 to 29.0 μg/g), increase in the Br2 and Br3 layers (23.9 μg/g to 31.9 μg/g), and then decrease to 15.1 μg/g in the lower layers. The lack of coupling between δ65Cu values and Cu concentrations may be ascribed to various physicochemical conditions in different layers. In the upper layers, Cu(I) enriched in light isotopes migrates downward with soil solutions under flooded conditions, leaving the soils of the Ap and Br1 layers enriched in heavy Cu isotopes. In the Br2 layer, the readsorption of light Cu isotopes on clay minerals results in decreased δ65Cu values and increased Cu concentrations. In the Br3-BCrg layers, Cu(I) can be oxidized to Cu(II). The homogeneous Cu isotopes in these layers may mainly result from equilibrium adsorption of Cu on clay minerals. The decreased δ65Cu values and Cu concentrations in the G layer are mainly attributed to groundwater transport in this layer. This study represents the Cu isotope variations in a paddy soil profile and the possible mechanism of Cu isotope fractionation.