Hydraulic Head Measurements Research Articles

Enhancing the groundwater storage estimates by integrating MT-InSAR, GRACE/GRACE-FO, and hydraulic head measurements in Henan Plain (China)

Aquifers supporting irrigated agriculture in Henan Plain in China (HNP) are under immense stress due to scarcity of surface water and over-pumping of groundwater. To assist in establishing a crop planting structure in this region that aligns more with the capacity of water resources, we have developed an enhanced methodology for estimating Groundwater Storage change (GWSC). This method integrates Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) inversion, Gravity Recovery and Climate Experiment (and Follow-on)/Global Land Data Assimilation System (GRACE/GLDAS) modelling, and hydraulic head measurements. The time-series comparisons based on well water levels and residual statistics of equivalent water thickness (EWT) validate the reliability of both MT-InSAR- and GRACE/GLDAS-derived GWSC, exhibiting a mean R-square of 0.8288 and an RMSE of 7.5 cm. The corrected and integrated GWSC significantly enhances the equal lon-lat tile of 1/4-degree (∼27 km) from CSR GRACE/GRACE-FO Mascon solution (CSR-M) to within 1 km in aquifers with known prior hydrogeological information. Furthermore, the geographic patterns analysis of GWSC in HNP using standard deviational ellipse (SDE) reveals that the central regions in HNP between the Yellow River and the Huaihe River suffer the most severe regional groundwater depletion. The depletion center has shifted to the northwest by 87 km, showing a trend of easing in the north-south direction and expanding in the east-west direction.

Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems

We present a model inversion algorithm, CKLEMAP, for data assimilation and parameter estimation in partial differential equation models of physical systems with spatially heterogeneous parameter fields. These fields are approximated using low-dimensional conditional Karhunen-Loève expansions (CKLEs), which are constructed using Gaussian process regression (GPR) models of these fields trained on the parameters' measurements. We then assimilate measurements of the state of the system and compute the maximum a posteriori (MAP) estimate of the CKLE coefficients by solving a nonlinear least-squares problem. When solving this optimization problem, we efficiently compute the Jacobian of the vector objective by exploiting the sparsity structure of the linear system of equations associated with the forward solution of the physics problem.The CKLEMAP method provides better scalability compared to the standard MAP method. In the MAP method, the number of unknowns to be estimated is equal to the number of elements in the numerical forward model. On the other hand, in CKLEMAP, the number of unknowns (CKLE coefficients) is controlled by the smoothness of the parameter field and the number of measurements, and is generally much smaller than the number of discretization nodes, which leads to a significant reduction of computational cost with respect to the standard MAP method. To show this advantage in scalability, we apply CKLEMAP to estimate the transmissivity field in a two-dimensional steady-state subsurface flow model of the Hanford Site by assimilating synthetic measurements of transmissivity and hydraulic head. We find that the execution time of CKLEMAP scales nearly linearly as N1.33, where N is the number of discretization nodes, while the execution time of standard MAP scales as N2.91. The CKLEMAP method improved execution time without sacrificing accuracy when compared to the standard MAP method.

Can local drain flow measurements be utilized to improve catchment scale modelling?

Tile drains constitute a shortcut from agricultural fields to surface water systems, significantly altering the transport pathways and fate of nitrate during transport. A correct representation of tile drainage flow is thus crucial for estimating nitrate load at the catchment scale and to identify optimal locations for N-mitigation measures. Drainage is a local process, controlled by local properties and drain configurations, which are rarely known for individual fields, making drainage flow and transport a challenging task in catchment scale models. This study tests the potential for improving drainage flow dynamics at catchment scale, by utilising local drainage flow measurements in a spatial calibration scheme. A distributed hydrological model, MIKE SHE, for the agricultural-dominated Norsminde catchment (145 km2) in Denmark, was calibrated using spatially distributed surrogate parameters (pilot points) to represent heterogeneity in the soil (top 3 m) and the deeper geology below 3 m. The model was calibrated using hydraulic heads, stream discharge, and measured drainage flow from eight drain catchments. Drain measurements were very important in guiding the calibration of top 3 m and subsurface pilot points located in the drainage fields, showing that drain flow hold information on both local (shallow) and regional (deeper) flow patterns. Contrarily, pilot points located outside the drained fields were mainly sensitive to the hydraulic head measurements and the summer water balance of the stream discharge on a catchment scale. Consequently, incorporation of the drain data improved local performance, but did not improve the parameterization and drain description of the entire catchment. Exploitation of the drain flow information is thus difficult beyond the drain catchments, and other approaches are needed to extrapolate and exploit the local data.

Predictive Deep Learning for High‐Dimensional Inverse Modeling of Hydraulic Tomography in Gaussian and Non‐Gaussian Fields

AbstractInverse modeling of hydraulic tomography (HT) is computationally expensive for estimating high‐dimensional hydrogeologic parameter fields. In this work, we develop a novel method called HT‐INV‐NN, which combines dimensionality reduction techniques with a predictive deep learning (DL) model to estimate high‐dimensional Gaussian and non‐Gaussian channel fields. The HT‐INV‐NN model consists of a predictor that directly learns the inverse process from hydraulic head measurements to latent variables of random fields, and a decoder that generates high‐dimensional parameter fields from predicted latent variables. For Gaussian spatially correlated fields, the decoder utilizes principal components derived from spatial covariance, and for non‐Gaussian channel fields, a generative adversarial network (GAN) is trained using generated realizations based on a training image (TI). The predictor is a deep neural network calibrated using the reference data obtained from HT forward simulations, which can be implemented in parallel. HT‐INV‐NN is successfully tested in multiple numerical experiments including steady‐state and transient HT for estimating Gaussian fields in 2D and 3D, as well as binary discontinuous or continuous non‐Gaussian channel fields. The training process is efficient, and the model structure demonstrates robustness for input data with perturbations. The model performance on multiple validation data sets are satisfactory when compared with other numerical and deep learning methods.

Modeling Water Flow and Solute Transport in Unsaturated Soils Using Physics‐Informed Neural Networks Trained With Geoelectrical Data

AbstractAccurate modeling of water flow and solute transport in unsaturated soils is of significant importance for precision agriculture, environmental protection and aquifer management. Traditional modeling approaches are considerably challenging since they require well‐defined boundaries and initial conditions. Physics‐informed neural networks (PINNs) have recently been developed to learn and solve forward and inverse problems also constrained to a set of partial differential equations and are more flexible than traditional modeling approaches. However, hydrological applications of PINNs used so far spatial measurements of hydraulic head, water content and/or solute concentrations, which were well distributed in the subsurface for training the system. Such measurements are hard to obtain in real‐world applications. Here, we propose to train PINNs with non‐invasive geoelectrical tools for simulating two‐dimensional water flow and solute transport during infiltration and redistribution processes with unknown initial conditions. Two‐dimensional flow and transport numerical simulations were used as benchmarks to examine the suitability of the described approach. Results have shown that the trained PINNs system was able to reproduce the spatiotemporal distribution of both water content and pore‐water salinity during both processes with high accuracy, using five time‐lapse geoelectrical measurements and matric head measurements at a single location. The trained PINNs system reconstructed the initial conditions of both state parameters at both processes. It was also able to separate the measured electrical signal into its two components, that is, water content and pore‐water salinity. The subsurface geoelectrical tomograms were significantly improved compared to those obtained from a classical inversion of the raw geoelectrical data.

Multitasking neural network to jointly map discrete fracture structures and matrix transmissivity by inverting hydraulic data acquired in 2D fractured aquifers. XNET-fracture

In this paper, we propose a new approach to simultaneously map the fracture/conduit network and the equivalent transmissivity of the rock matrix from hydraulic head measurements acquired during pumping/injection tests in fractured aquifers. The algorithm relies on the use of a multitask neural network to directly approximate the joint inversion operator that links hydraulic head data to aquifer hydraulic properties. In which, the hydraulic head responses are used as input data while the output consists of the fracture structure and matrix transmissivity field. The multitasking is formed by fusing two single-task neural networks, both based on convolutional encoder-decoder architectures, one of which processes the fracture map inversion and the other the transmissivity map. Training neural network then relies on a large database, which consists of thousands of synthetic aquifers characterized by the presence of the fracture/conduit network and a heterogeneous transmissivity field attributed to the rock matrix, both randomly generated. In these aquifer models, the groundwater flow equation is solved in a discrete-continuum concept to numerically simulate the hydraulic head responses associated with sequential pumping/injection tests.Multitasking succeeds in reconstructing the fracture architecture and matrix transmissivity field with higher accuracy in comparison to conventional single-task networks. The difference is due to the transfer mechanism involved in the multi-task network, where the information shared between the tasks effectively enhances the accuracy of both reconstructions. However, as with other inversion techniques, the result accuracy depends on the quality and quantity of the hydraulic head data used in the inversion, as well as the prior information involved in featuring the training models.

Investigating river–aquifer interactions using heat as a tracer in the Silala river transboundary basin

AbstractThis article reviews scientific studies in which heat was used as a natural tracer to investigate stream–aquifer interactions in the Silala River in Chile and provide evidence that was used to support a legal dispute between Chile and Bolivia over the status and use of the waters of this watercourse. Streambed temperature time series at various locations downstream of the Chile‐Bolivia international border showed that water flows downwards through the streambed sediments, from the river towards the fluvial aquifer. These findings are consistent with hydraulic head measurements performed at the study site. Additionally, fiber‐optic distributed temperature sensing (FO‐DTS) methods were employed to observe river temperatures with a spatial resolution of the order of 0.5 m in a river reach of ~1.3 km. FO‐DTS technology allowed detection of various warm springs that discharged their waters into the Silala River, as well as the location of an artesian well that supplies the river with ~90 L/s of deep groundwater at ~20 °C. The results provided improved understanding of the Silala River hydrogeology, and were used to calibrate and validate a groundwater model of the system, reported elsewhere. Both methodologies demonstrated that the river is indeed a system of surface waters and groundwaters interacting as a unitary whole, a key aspect of the definition of an international watercourse, and generated valuable scientific evidence to support a major international legal dispute.This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods

What Is the Impact of Leaky Sewers on Groundwater Contamination in Urban Semi-Confined Aquifers? A Test Study Related to Fecal Matter and Personal Care Products (PCPs)

Urban areas exercise numerous and strong pressures on water bodies, implying that different external anthropogenic factors also stress groundwater. Sewerage networks play an important role, being the place of wastewater flow. When sewerage deterioration conditions occur, aquifers can be contaminated by contaminants contained within wastewater. The study aims to verify the impact of sewerage leaks in urban semi-confined aquifers through a multidisciplinary approach. Geological, hydrogeological, hydrochemical, microbiological, and biomolecular investigations are carried out in a test site close to a sewer pipe, from February to October 2022. Microbiological analyses are carried out on a monthly basis, contextually to hydraulic head measurements in purpose-drilled piezometers. The presence of sandy intercalations and the prevalence of silt within the outcropping (about 10 m thick) aquitard makes the aquifer vulnerable to percolation from leaky sewers, therefore causing persistent microbial contamination in groundwater. The presence of fecal indicators (including pathogenic genera), corrosive and human-associated bacteria markers, is detected. The magnitude of microbiological impact varies over time, depending on hydrogeological factors such as dilution, hydrodynamic dispersion, and variation of the groundwater flow pathway at the site scale. As for personal care products, only Disodium EDTA is detected in wastewater, while in groundwater the concentrations of all the analyzed substances are lower than the instrumental detection limit.

Characterizing groundwater heat transport in a complex lowland aquifer using paleo-temperature reconstruction, satellite data, temperature–depth profiles, and numerical models

Abstract. Heat is a naturally occurring, widespread groundwater tracer that can be used to identify flow patterns in groundwater systems. Temperature measurements, being relatively inexpensive and effortless to gather, represent a valuable source of information which can be exploited to reduce uncertainties on groundwater flow, and, for example, support performance assessment studies on waste disposal sites. In a lowland setting, however, hydraulic gradients are typically small, and whether temperature measurements can be used to inform us about catchment-scale groundwater flow remains an open question. For the Neogene Aquifer in Flanders, groundwater flow and solute transport models have been developed in the framework of safety and feasibility studies for the underlying Boom Clay formation as a potential host rock for geological disposal of radioactive waste. However, the simulated fluxes by these models are still subject to large uncertainties as they are typically constrained by hydraulic heads only. In the current study, we use a state-of-the-art 3D steady-state groundwater flow model, calibrated against hydraulic head measurements, to build a 3D transient heat transport model, for assessing the use of heat as an additional state variable, in a lowland setting and at the catchment scale. We therefore use temperature–depth (TD) profiles as additional state variable observations for inverse conditioning. Furthermore, a Holocene paleo-temperature time curve was constructed based on paleo-temperature reconstructions in Europe from several sources in combination with land surface temperature (LST) remotely sensed monthly data from 2001 to 2019 (retrieved from NASA's Moderate Resolution Imaging Spectroradiometer, MODIS). The aim of the research is to understand the mechanisms of heat transport and to characterize the temperature distribution and dynamics in the Neogene Aquifer. The simulation results clearly underline advection/convection and conduction as the major heat transport mechanisms, with a reduced role of advection/convection in zones where flux magnitudes are low, which suggests that temperature is also a useful indicator in a lowland setting. Furthermore, the performed scenarios highlight the important roles of (i) surface hydrological features and withdrawals driving local groundwater flow systems and (ii) the inclusion of subsurface features like faults in the conceptualization and development of hydrogeological investigations. These findings serve as a proxy of the influence of advective transport and barrier/conduit role of faults, particularly for the Rauw fault in this case, and suggest that solutes released from the Boom Clay might be affected in similar ways.

Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties

AbstractIdentification of a heterogeneous conductivity field and reconstruction of a contaminant release history are key aspects of subsurface remediation. These two goals are achieved by combining model predictions with sparse and noisy hydraulic head and concentration measurements. Solution of this inverse problem is notoriously difficult due to, in part, high dimensionality of the parameter space and high computational cost of repeated forward solves. We use a convolutional adversarial autoencoder (CAAE) to parameterize a heterogeneous non‐Gaussian conductivity field via a low‐dimensional latent representation. A three‐dimensional dense convolutional encoder‐decoder (DenseED) network serves as a forward surrogate of the flow and transport model. The CAAE‐DenseED surrogate is fed into the ensemble smoother with multiple data assimilation (ESMDA) algorithm to sample from the Bayesian posterior distribution of the unknown parameters, forming a CAAE‐DenseED‐ESMDA inversion framework. The resulting CAAE‐DenseED‐ESMDA inversion strategy is used to identify a three‐dimensional contaminant source and conductivity field. A comparison of the inversion results from CAAE‐ESMDA with physical flow and transport simulator and from CAAE‐DenseED‐ESMDA shows that the latter yields accurate reconstruction results at the fraction of the computational cost of the former.

Aquifer feature estimation in Basara basin using a groundwater modeling system for smart agriculture

We estimate aquifer characteristics in the Basara basin using Groundwater Modeling System (GMS) software in this article. The 960 hydraulic head measurements are taken from 16 observation wells across 60 stress periods in the proposed research. Calibration of aquifer parameters produced hydraulic conductivity values ranging from 1.96 to 117.8 m/d. The specific yield values varied from (=0.31) to (>=0.06). The daily recharge rates ranged from (3.33 *10-4) to (1 *10-3). We examined six pumping scenarios for 2020-2024. (S1–S6). Groundwater levels often dropped further as pumping rates rose and recharging rates decreased. The lower contour elevation for scenario 6 is 745.43 m, while it was 748.39, 753.56, 753.56, 753.56, and 753.86 m for scenarios 5, 4, 3, 2, and 1. The findings of this study help policymakers plan for potential shortages in semi-arid areas. The available options can be reproduced elsewhere to provide a more precise and reliable groundwater solution.

Inferring fault hydrology using groundwater age tracers

Faults can act as barriers to cross-fault flow, conduits to along-fault flow or both. Groundwater age inferred from hydrochemical data is one of the few tools that can estimate both barrier and conduit behaviour of the fault and associated fault damage zone. Conduit behaviour has been previously inferred from the existence of young water in deep aquifers or old water in shallow aquifers. While the ability of groundwater age to estimate the barrier behaviour of faults has been illustrated theoretically, it has not yet been observed in the field. Here, conceptual models of hydraulic head and groundwater age perpendicular to and across faults are discussed in terms of the resistance to flow across the fault (fault width divided by cross-fault transmissivity), the resistance to flow around the fault (fault length divided by aquifer transmissivity) and the recharge regime. To examine these conceptual models, field measurements of hydraulic head, 14C, chloride and electrical conductivity in groundwater are presented along transects across three contrasting fault systems. Large cross-fault head and age gradients are apparent at two of the three sites. At one of these sites, where regional flow is mostly parallel to the fault, 14C age and chloride data are used to estimate the rate of cross-fault flow. At the third site, there may not be a barrier to cross-fault flow, as there is no clear increase in either head or age gradient. The studies illustrate the importance of high data density to characterise fault zones, particularly for groundwater age which will vary both with depth and horizontal location along a transect. Although our data does not indicate conduit behaviour, such behaviour has been suggested based on groundwater chemistry at other locations along the fault strike at two of the sites.

Hierarchical Bayesian Inversion of Global Variables and Large‐Scale Spatial Fields

AbstractBayesian inversion is commonly applied to quantify uncertainty of hydrological variables. However, Bayesian inversion is usually focused on spatial hydrological properties instead of hyperparameters or non‐gridded physical global variables. In this paper, we present a hierarchical Bayesian framework to quantify uncertainty of both global and spatial variables. We estimate first the posterior of global variables and then hierarchically estimate the posterior of the spatial field. We propose a machine learning‐based inversion method to estimate the joint distribution of data and global variables directly without introducing a statistical likelihood. We also propose a new local dimension reduction method: local principal component analysis (local PCA) to update large‐scale spatial fields with local data more efficiently. We illustrate the hierarchical Bayesian formulation with three case studies: one with a linear forward model (volume averaging inversion) and two with non‐linear forward models (pumping tests and hydraulic head measurements), including a 3D case. Results show that quantifying global variables uncertainty is critical for assessing uncertainty on predictions. We show how the local PCA approach accelerates the inversion process. Furthermore, we provide an open‐source Python package (https://github.com/lijingwang/hierarchicalBayes.git) on the hierarchical Bayesian framework including three case studies.

过度渗透性回填的多级振弦式压力计水位测量的偏差

过度渗透性回填的多级振弦式压力计水位测量的偏差

Constraining the response of continental-scale groundwater flow to climate change

Numerical models of groundwater flow play a critical role for water management scenarios under climate extremes. Large-scale models play a key role in determining long range flow pathways from continental interiors to the oceans, yet struggle to simulate the local flow patterns offered by small-scale models. We have developed a highly scalable numerical framework to model continental groundwater flow which capture the intricate flow pathways between deep aquifers and the near-surface. The coupled thermal-hydraulic basin structure is inferred from hydraulic head measurements, recharge estimates from geochemical proxies, and borehole temperature data using a Bayesian framework. We use it to model the deep groundwater flow beneath the Sydney–Gunnedah–Bowen Basin, part of Australia’s largest aquifer system. Coastal aquifers have flow rates of up to 0.3 m/day, and a corresponding groundwater residence time of just 2,000 years. In contrast, our model predicts slow flow rates of 0.005 m/day for inland aquifers, resulting in a groundwater residence time of sim 400,000 years. Perturbing the model to account for a drop in borehole water levels since 2000, we find that lengthened inland flow pathways depart significantly from pre-2000 streamlines as groundwater is drawn further from recharge zones in a drying climate. Our results illustrate that progressively increasing water extraction from inland aquifers may permanently alter long-range flow pathways. Our open-source modelling approach can be extended to any basin and may help inform policies on the sustainable management of groundwater.

Inverse Modeling of Grout Curtain Hydraulic Conductivity Evolution Considering the Calcium Leaching Effect

The calcium leaching effect inevitably increases the grout curtain hydraulic conductivity. It is diffucult to sample and obtain the leaching-related calculation parameters for deep-buried grout curtains. This study introduced the inversion method into the calcium leaching analysis to get proper leaching-related calculation parameters and accurate results. An inverse analysis model was proposed using the genetic algorithm (GA) and finite element analysis technology to solve the calcium leaching problems. The objective function is constructed using the hydraulic head and leakage quantity time-series measurements, which improves the uniqueness and reliability of the inverse results. The proposed method was applied to the inverse analysis of the hydraulic conductivity evolution of the grout curtain in a concrete dam foundation. The predicted water heads and leakage quantity are consistent with the monitored data, indicating the rationality of this simulation. The grout curtain hydraulic conductivity prediction in 100 years is also presented. The results illustrate the feasibility of the proposed method for determining leaching-related parameters and the hydraulic conductivity prediction in the leaching process.

A density-dependent multi-species model to assess groundwater flow and nutrient transport in the coastal Keauhou aquifer, Hawai\u2018i, USA

Fresh groundwater is a critical resource supporting coastal ecosystems that rely on low-salinity, nutrient-rich groundwater discharge. This resource, however, is subject to contamination from point- and nonpoint-sources such as on-site sewage disposal systems (OSDS) and urban developments. Thus, the significance of flow and transport processes near the coastline due to density effects and water circulation in a complex hydrogeologic system was investigated. A three-dimensional, density-dependent groundwater model was developed for the Keauhou basal aquifer (Hawai‘i Island, USA), where hydraulic head, salinity, nutrient concentrations, and submarine spring flux rates were used as calibration variables to best constrain parameters and produce a comprehensive aquifer management tool. In contrast, a freshwater-only model failed to properly simulate nutrient transport, despite the reasonable success in calibrating hydraulic head measurements. An unrealistic value for hydraulic conductivity was necessary for freshwater-only calibration, proving that hydraulic conductivity is a process-based variable (i.e., depends on model conceptualization and the simulated processes). The density-dependent model was applied to assess relative contaminant source contributions, and to evaluate aquifer response concerning water levels and quality due to changing environmental conditions. Nutrients detected in the aquifer are primarily sourced from OSDS, which was supported by a nitrogen isotope mixing model. Additionally, effects of sea-level rise emphasized the complexity of the study site and the importance of model boundaries. While the model is developed and applied for West Hawai‘i, the adapted approaches and procedures and research findings are applicable to other coastal aquifers.

Apportioning deformation among depth intervals in an aquifer system using InSAR and head data

Land surface subsidence due to excessive groundwater pumping is an increasing concern in California, USA. Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technique for measuring centimeter-to-millimeter surface deformation at 10–100 m spatial resolution. Here, a data-driven approach that attributes deformation to individual depth intervals within an aquifer system by integrating head data acquired from each of three screened intervals in a monitoring well with InSAR surface deformation measurements was developed. The study area was the Colusa Basin in northern Central Valley. To reconstruct the surface deformation history over the study area, 13 ALOS-PALSAR scenes acquired between 2006 and 2010 were processed. Up to ~3-cm year−1 long-term subsidence and up to ~6 cm seasonal subsidence were observed using the InSAR technique. The technique developed in this paper integrates the InSAR-observed seasonal deformation rate and the co-located head measurements in multiple depth intervals to estimate the elastic skeletal storage coefficient, the time delay between the head change and the observed deformation, and subsequently the deformation of each depth interval. This technique can be implemented when hydraulic head measurements within each depth interval are not correlated with each other. Using this approach, the depth interval that contributed the most to the total subsidence, as well as storage parameters for all intervals, are estimated. The technique can be used for identification of the depth interval within the aquifer system responsible for deformation.

Groundwater level assessment and prediction in the Nebraska Sand Hills using LIDAR-derived lake water level

The spatial variability of groundwater levels is often inferred from sparsely located hydraulic head observations in wells. The spatial correlation structure derived from sparse observations is associated with uncertainties that spread to estimates at unsampled locations. In areas where surface water represents the nearby groundwater level, remote sensing techniques can estimate and increase the number of hydraulic head measurements. This research uses light detection and ranging (LIDAR) to estimate lake surface water level to characterize the groundwater level in the Nebraska Sand Hills (NSH), an area with few observation wells. The LIDAR derived lake groundwater level accuracy was within 40 cm mean square error (MSE) of the nearest observation wells. The lake groundwater level estimates were used to predict the groundwater level at unsampled locations using universal kriging (UK) and kriging with an external drift (KED). The results indicate unbiased estimates of groundwater level in the NSH. UK showed the influence of regional trends in groundwater level while KED revealed the local variation present in the groundwater level. A 10-fold cross-validation demonstrated KED with better mean squared error (ME) [−0.003, 0.007], root mean square error (RMSE) [2.39, 4.46], residual prediction deviation (RPD) [1.32, 0.71] and mean squared deviation ratio (MSDR) [1.01, 1.49] than UK. The research highlights that the lake groundwater level provides an accurate and cost-effective approach to measure and monitor the subtle changes in groundwater level in the NSH. This methodology can be applied to other locations where surface water bodies represent the water level of the unconfined aquifer and the results can aid in groundwater management and modeling.

Groundwater contributions of flow and nitrogen in a headwater agricultural watershed

AbstractNonpoint sources of nitrogen (N) and other nutrients are a major source of water pollution within the Chesapeake Bay watershed and other basins around the world. Human activities associated with agricultural practices can account for a large percentage of N loadings delivered to streams and rivers. This work aims to improve understanding of N transport from groundwater to surface waters, quantifying the principal hydrological processes driving water and N fluxes into and out of a headwater agricultural stream reach. The study site is a 175‐m stream reach in a heavily cultivated 40‐ha watershed in east‐central Pennsylvania. This subwatershed is underlain by fractured shale bedrock, and receives most of its baseflow from groundwater, either by diffuse matrix discharge through the streambed or by localized discharge through riparian seeps. Samples of stream, seep, and shallow groundwater were collected approximately monthly under steady hydrologic conditions in 2017. Calculated matrix flow from hydraulic head and conductivity measurements paired with differential stream gauging was used to solve for the riparian seep flux using a mass balance approach. Riparian seep fluxes ranged from 45 to 217 m3/d, transporting 0.6–4.2 kg N d−1 of nitrate‐N from the fractured bedrock aquifer to the stream. Hydrochemical data suggest that the stream is mainly disconnected from the underlying aquifer and that seeps supply essentially all water and N to the system. Seeps are likely sourced with N in nearby agricultural fields and accelerated through the system with shorter residence times than shallow groundwater. Water isotope data reinforced this notion. This study underscores the importance of agriculture as a source of N to ground and surface waters. Identifying source areas that are causing groundwater enrichment of N and seep areas where N discharges to streams is beneficial for developing N pollution mitigation strategies and implementing management practices that aim to reduce nutrient loads to the Chesapeake Bay.