Estimation Of Hydraulic Conductivity Research Articles

Evaluation of infiltration testing methods for design of stormwater drywell systems

The current infiltration testing and drywell design methods do not always provide accurate estimates of the capacity of production-scale dry wells. In numerous cases, the predicted capacities appear to be considerably lower than the actual drywell capacity, resulting in more drywells than required and significantly higher construction and long-term maintenance costs. In other cases, the uncertainty in the measurements results in the predicted capacities being higher than the actual drywell capacity, resulting in fewer drywells being required. In this study, we discuss the reasons for these inaccurate predictions of drywell performance using current methods, as well as the results of recent studies to develop improved methods for predicting the capacity of stormwater infiltration drywells. These studies, funded by the United States Environmental Protection Agency (EPA) and the Los Angeles County Safe Clean Water Program (SCW), include numerical simulations and field studies in California. These test methods rely on steady-state infiltration tests in small-diameter boreholes and produce an estimate of the saturated hydraulic conductivity, which can be used to reliably predict the performance of large-diameter drywells. The methods were validated by testing small- and large-diameter test wells in close proximity using three different drilling methods. The results of this study describe the design and conduct of a borehole infiltration test that accurately represents drywell performance. Furthermore, based on a combination of numerical simulations and field studies, reduction factors were developed to account for test uncertainty, test duration, analytical error, field variability, potential for groundwater mounding, and clogging.

A percolation model of unsaturated hydraulic conductivity using three-parameter Weibull distribution

Accurate estimation of unsaturated hydraulic conductivity, K(Sw), is crucial for modeling water and solute transport in variably-saturated soils. In this study, a new model for the estimation of K(Sw) based on percolation theory and critical path analysis is presented, in which the pore-throat size distribution is described by the three-parameter Weibull probability density function. To evaluate the performance of the proposed K(Sw) model, two datasets including more than three hundred samples are analyzed. The first dataset includes 240 pore-network simulations, while the second dataset consists of 101 soil samples from the UNSODA database. For the pore-network simulations, for which both pore-throat size distributions and capillary pressure curves are available, we estimate K(Sw) from the capillary pressure curve with RMSLE = 0.54 slightly better than the estimations obtained from the pore-throat size distribution with RMSLE = 0.6. For the soil samples, since only the capillary pressure curve is measured, we estimate K(Sw) from the capillary pressure curve and evaluate three previously proposed approaches for determining the exponent, α, in Poiseuille's law. We find that RMSLE = 0.98 for α = 3, RMSLE = 0.87 for α = 2(4 − D) − (3 − D)/(2D − 3), and RMSLE = 1.06 for α = 4 − 0.74D, in which D is the fractal dimension characterizing the pore space. Comparing the results with previous studies in which the power-law probability density function was used to characterize capillary pressure curve and pore sizes shows that the proposed three-parameter Weibull distribution, in conjunction with the CPA analysis, may more accurately estimate K(Sw) for a wide variety of soils ranging from very fine to very coarse textures.

Multi-step modeling of well logging data combining unsupervised and deep learning algorithms for enhanced characterization of the Quaternary aquifer system in Debrecen area, Hungary

AbstractIn this research, a multi-step modeling approach is followed using unsupervised and deep learning algorithms to interpret the geophysical well-logging data for improved characterization of the Quaternary aquifer system in the Debrecen area, Hungary. The Most Frequent Value-Assisted Cluster Analysis (MFV-CA) is used to map lithological variations within the aquifer system. Additionally, the Csókás method is used to discern both vertical and horizontal fluctuations in hydraulic conductivity. MFV-CA is introduced to cope with the limitation of the conventional Euclidean distance-based k-means clustering known for its low resistance to outlying values, resulting in deformed cluster formation. However, the computational time and demands of MFV-CA are evident, making them costly and time-consuming. As a result, Deep Learning (DL) methods are suggested to provide fast characterization of the groundwater aquifers. These methods include Multi-Layer Perceptron Neural Networks (MLPNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM), which are implemented for classification and regression. The classification categorized the inputs into three distinct lithologies trained initially by the results of MFV-CA. At the same time, the regression model offered a continuous estimations of hydraulic conductivity trained by the results of the Csókás model. The results demonstrated significant compatibility between the outcomes derived from the clustering and Csókás approaches and DL algorithms. Accordingly, the lithofacies and hydraulic conductivity variations across the main hydrostratigraphical units are mapped. This integration enhanced the understanding of the groundwater system, offering promising inputs for groundwater and development and management.

Geophysical estimation of 2D hydraulic conductivity for groundwater assessment in hard rock

Geophysical estimation of 2D hydraulic conductivity for groundwater assessment in hard rock

Numerical estimation of the equivalent hydraulic conductivity for canal concrete lining with cracks

The equivalent saturated hydraulic conductivity of concrete lining (Ksce) is a key parameter to estimate seepage loss, while few studies have quantified the effect of cracks on Ksce, potentially leading to significant errors in canal seepage estimation. A three-dimensional numerical model is employed to simulate seepage loss from canals lined with concrete, and then to obtain the value of Ksce by an equivalent method. A total of 58,400 scenarios are simulated for different concrete lining and crack conditions (e.g., crack length, crack width, crack location, hydraulic conductivity of concrete (Ksc), and underlying soil (Ksoil)), and the corresponding Ksce values are estimated. Results show that the Ksce value determined by the equivalent method is reliable in calculating seepage loss from concrete lined canals. Ksce increases linearly with crack length with a slope Ck, and Ck exhibits a power function relationship with crack width, reaching a maximum value when the crack width surpasses the critical threshold, which ranges from 0.424 to 0.435 mm. When the crack width follows a log-normal distribution, the logarithmic value of Ck increases linearly with the mean value of the logarithmic crack width. The influence of crack location and Ksc on Ksce can be negligible. The maximum increment of Ksce of a crack is proportional to Ksoil. Considering the variation of Ksce with crack parameters and Ksoil, equations to determine the value of Ksce are established by adding the increment of Ksce caused by cracks to Ksc. This study establishes a quantitative relationship between Ksce and crack parameters, thereby enhancing the accuracy of seepage loss calculations in lined canal under different damage status.

Disentangling slope‐scale spatial variability of saturated hydraulic conductivity in the black soil region of northeast China using noise‐assisted multivariate empirical mode decomposition

AbstractReliable estimates of saturated hydraulic conductivity (Ks) are usually difficult to obtain, as Ks is regulated by a variety of soil processes acting at different spatial scales that may obscure each other's impacts. We hypothesized that these scale‐specific relationships could be well characterized with the aid of the noise‐assisted multivariate empirical mode decomposition (NA‐MEMD), thereby serving as a solid foundation for an accurate Ks prediction. The objective was to evaluate whether the incorporation of NA‐MEMD could improve the estimation of Ks based on the multiscale associations it unraveled. On a typical slope transect of 860 m in the black soil region of northeast China, Ks, mollic epipedon thickness, bulk density, soil organic carbon content, total (ϕ) and effective porosities (ϕe), and particle size distribution were investigated at every 20 m. Prior to NA‐MEMD, Ks was most strongly correlated with ϕe, and the linear regression models based on ϕe solely were satisfactory for Ks estimation at the scale of investigation. Adding other predictors significantly improved Ks prediction in calibration, but impaired it in validation. Upon decomposition by NA‐MEMD, Ks was found to be significantly associated with each attribute at two scales of oscillation at least. Summing up the estimates of each Ks component derived from the properties at the equivalent oscillation scales, the results outperformed the traditional multiple linear regressions made at the investigation scale, when the same sets of predictors were used. The application of NA‐MEMD, moreover, could save the tedious measurements of ϕe and ϕ. Excluding these two porosity‐related properties, Ks estimates obtained by incorporating NA‐MEMD were statistically similar or even better than those involving them before NA‐MEMD. These findings demonstrate the great potential of NA‐MEMD in untangling scale‐dependent relationships of Ks with various processes and hold important implications for future estimations of Ks and other hydraulic properties in the black soil region of northeast China and similar regions.

Improved solute transport modeling through joint estimation of hydraulic conductivity and dispersivities from tracer and ERT data

The hydraulic conductivity (K) has been recognized as one of the controlling parameters that significantly impact the transport behavior in groundwater systems. To improve the predictive ability of a transport model, concentration measurements are commonly used in inverse modeling to characterize the K distribution. In addition to K, the spread of the solute plume also depends on dispersion coefficients. The evolution of the plume morphology can only be satisfactorily reproduced with appropriate dispersivities. Dispersivity values or distributions rely on the resolution of the inferred K distribution. Dispersivities were typically treated as spatially uniform in inverse modeling and jointly estimated with K. Although employing spatially variable dispersivities may hold the potential to better capture the solute transport behavior, it may significantly increase the number of unknown parameters for inverse modeling. Solving such an ill-posed inversion problem is challenging, especially when the amount of intrusive borehole-based measurements is limited. As a non-intrusive, cost-effective, and high sampling density method, time-lapse geophysical technique (e.g., electrical resistivity tomography, ERT) has not yet drawn much attention as a useful source of information for dispersivity estimation. In this study, we developed a joint hydrogeophysical inversion framework to simultaneously estimate spatially variable K and dispersivities by integrating tracer-concentration and ERT-derived potential data. We evaluated the proposed framework by conducting numerical tracer experiments in a highly heterogeneous aquifer. Results show that using limited tracer concentration data alone failed to resolve the fine structure in K field. In this case, there was little difference in the prediction performance of solute transport by considering spatially uniform or variable dispersivities. In contrast, the K distribution was estimated at a significantly higher resolution when integrating tracer-concentration and ERT data, contributing to a remarkable improvement in transport modeling. Powered by the sufficient amount of ERT measurements, jointly estimating spatially variable dispersivities further enhanced the reproduction of tracer plumes and breakthrough curves.

Novel physics informed-neural networks for estimation of hydraulic conductivity of green infrastructure as a performance metric by solving Richards–Richardson PDE

Novel physics informed-neural networks for estimation of hydraulic conductivity of green infrastructure as a performance metric by solving Richards–Richardson PDE

Field surveys in heterogeneous rock masses aimed at hydraulic conductivity assessment

In heterogeneous and fractured rock masses, joints are the most important elements controlling the hydraulic conductivity. Joints parameters are therefore crucial for estimating transport of mass and energy in many technical problems (e.g., slope stability, tunnels, geothermal and oil and gas studies). Hydraulic conductivity conceptual models follow well understood generalizations, but their quantitative estimation is not simple. Both laboratory and field tests have many limitations due to representativeness and scale effects. This study proposes a methodology for a preliminary hydraulic conductivity estimation in fractured and/or heterogeneous rock masses through a combination of in-situ geomechanical and geophysical measurements. Contact and no-contact geomechanical surveys were coupled with electric resistivity topographies in two selected test sites within a Mexican geothermal area in the framework of the Ge-Mex H2020 project. The test sites are representative of two different geological settings: a faulted rock mass with expected high hydraulic conductivity in proximity of the faulted areas and an abandoned marble quarry, with very good mechanical characteristics and negligible degree of fracturing. Moreover, both of them are located in remote areas with limited accessibility, in which rapid, time and cost-effective procedures are welcomed and recommended. The preliminary findings of this study were promising: estimated hydraulic conductivities were compared with independent laboratory measurements performed by other researchers showing a good correspondence and reliability. The proposed methodology demonstrated its reliability in decision making, in the technical support and its economical applicability also in similar difficult logistic situations.Article HighlightsA combination of in-situ geomechanical and geophysical measurements was adopted for a preliminary hydraulic conductivity estimation;Obtained field data were compared with independent laboratory estimations to assess the reliability of the proposed approach and compare representativeness and scale effects;Even given the difficult logistic conditions and the limitations of the available comparisons the proposed methodology demonstrated its reliability in decision making.

Estimation of pollution sources and hydraulic conductivity field in a coastal aquifer under tidal effects

In the past decades, most of the efforts have often been invested in the joint identification of contamination and hydraulic conductivity field in inland subsurface aquifers. However, the booming economy and dense population around the coast have brought a new challenge to the forefront: the serious pollution of coastal aquifers. Coastal aquifers are highly vulnerable ecological areas, which are affected by inland pollutants carried by groundwater during its entry into the sea and are also influenced by seawater intrusion. This article addresses the inverse problem of coastal aquifer pollution, which is subject to tidal effects, using the Iterative Local Updating Ensemble Smoother (ILUES) algorithm. The adopted ILUES algorithm is leveraged to efficiently extract pollutant concentration and salinity data for the identification of the source release history and characterization of the hydraulic conductivity field of a site with multiple pollutant sources. This study is based on a variant of the traditional benchmark problem, analyzing the influence of algorithm parameters, tidal effects, and heterogeneity on data assimilation. The article further demonstrates the great potential of ILUES to address new challenges. In addition, the significance of multi-level monitoring wells for the estimation of hydraulic conductivity in coastal multi-layered heterogeneous aquifers is explained.

Exploration of Ground Water Potential of Federal University of Technology, Owerri and Its Environs Using Resistivity Survey

The research focused on evaluating the groundwater potential in the vicinity of Federal University of Technology Owerri (FUTO) using a resistivity survey method. The investigation involved the utilization of eleven Schlumberger vertical electrical soundings (VES). The data collection was conducted using an ABEM Terrameter SAS4000 and then processed with IPI2Win Software, which relies on the conventional theory of curve matching. This process aimed to determine the depth, quality, and subsurface formations of the groundwater. The VES interpretation results revealed the presence of 3-5 geoelectric layers above the aquiferous layers. Various curve types were identified in the study area, including monotonically ascending (AA), bowed-ascending (HA), bowl-bell (HK), and ascending-bell (AK) curves. The majority of the field curves were found to be of the A-shaped type, signifying specific aquifer characteristics. The study indicated that the depth to the water table is relatively shallow in the Ihiagwa area, with an average depth of 7.26 meters. In contrast, the aquifer's depth is deeper around Avu, Nekede, and Eziobodo areas, with a mean depth of 32.33 meters. Obinze had very deep aquifers, with an average depth of 57.75 meters. Furthermore, the study established a relationship between aquifer characteristics and geoelectric parameters, allowing for the estimation of hydraulic conductivity and transmissivity values at all the sounding locations, even in areas lacking boreholes. These hydraulic characteristics showed that the aquifer had protective capacities ranging from 0.036 to 0.509 mhos, transmissivity values ranging from 11856.32 to 28661.28 m2/day, and hydraulic conductivity ranging from 13.47 to 1009.2 m/day. These values suggested that the aquifer materials are highly permeable, facilitating fluid movement within the aquifer. This research demonstrated the effectiveness of surface geophysics in estimating aquifer hydraulic characteristics, particularly in situations where pumping test data are unavailable. It also highlighted the vulnerability of the aquifer to surface contaminants. The findings are expected to be valuable for the long-term planning of groundwater exploitation projects in the study area.

Infiltration Measurements during Dry Conditions in an Urban Park in Ljubljana, Slovenia

A thorough understanding of the hydrologic mechanisms that control the movement of water through the soil is essential for developing effective stormwater management strategies. Infiltration is critical for determining the amount of water entering the soil and controlling surface runoff. Spatial and temporal variations in soil properties strongly affect infiltration rates, which underscores the importance of evaluating field-specific values for hydraulic conductivity, which are also highly dependent on the chosen measurement and evaluation methods. The objective of this study is to determine and compare soil hydraulic conductivity under dry conditions using two field measurement techniques, namely the double-ring infiltrometer (DRI) and the mini-disk infiltrometer (MDI). The results demonstrate the importance of performing multiple replicates of infiltration tests, especially during the dry season, as the initial dry surface caused deviations in hydraulic conductivity estimates for both methods used (DRI and MDI). Significant spatial variability was observed within the radius of the test replicates over short distances (<1 m). In addition, experimental infiltration curves for a selected site were used to evaluate and compare soil hydraulic parameters through infiltration modeling. In general, the Philip, Green-Ampt, and Smith-Parlange theoretical models showed a better fit to the experimental DRI data than the semi-empirical Horton model.

A New Approach for Seepage Parameters Inversion Analysis Using Improved Whale Optimization Algorithm and Support Vector Regression

Seepage is the primary cause of dam failures. Conducting regular seepage analysis for dams can effectively prevent accidents from occurring. Accurate and rapid determination of seepage parameters is a prerequisite for seepage calculation in hydraulic engineering. The Whale Optimization Algorithm (WOA) was combined with Support Vector Regression (SVR) to invert the hydraulic conductivity. The good point set initialization method, a cosine-based nonlinear convergence factor, the Levy flight strategy, and the Quasi-oppositional learning strategy were employed to improve WOA. The effectiveness and practicality of Improved Whale Optimization Algorithm (IWOA) were evaluated via numerical experiments. As a case study, the seepage parameters of the Dono Dam located on the Baishui River in China were inversed, adopting the proposed inversion model. The calculated seepage field was reasonable, and the relative error between the simulated head and the measured value at each monitoring point was within 2%. This new inversion method is more feasible and accurate than the existing hydraulic conductivity estimation methods.

Enhancing estimation accuracy of nonstationary hydrogeological fields via geodesic kernel-based Gaussian process regression

In this study, the combined application of the geodesic kernel and Gaussian process regression was explored to estimate nonstationary hydraulic conductivity fields in two-dimensional hydrogeological systems. Specifically, a semianalytical form of the geodesic distance based on the intrinsic geometry of the manifold was derived and used to define positive definite geodesic covariance matrices that were used for the Gaussian process regression. Furthermore, the proposed approach was applied to a series of synthetic hydraulic conductivity estimation problems. The results show that the incorporation of secondary information, such as interpretations of geophysical explorations or geological surveys, can considerably improve the accuracy of the estimation, especially in nonstationary fields. In addition, groundwater flow and solute transport simulations based on estimated hydraulic conductivity fields reveal that the accuracy of the simulation is strongly affected by the inclusion of secondary information. These results suggest that incorporating secondary information into manifold geometry can remarkably improve the accuracy of the estimation and provide new insights into the underlying structure of geological data. This proposed approach has critical implications for hydrogeological applications, such as groundwater resource management, safety assessments, and risk management strategies related to groundwater contamination.

Evaluation of Hydraulic Conductivity Estimates from Various Approaches with Groundwater Flow Models.

Significant efforts have been expended for improved characterization of hydraulic conductivity (K) and specific storage (Ss) to better understand groundwater flow and contaminant transport processes. Conventional methods including grain size analyses (GSA), permeameter, slug, and pumping tests have been utilized extensively, while Direct Push-based Hydraulic Profiling Tool (HPT) surveys have been developed to obtain high-resolution K estimates. Moreover, inverse modeling approaches based on geology-based zonations, and highly parameterized Hydraulic Tomography (HT) have also been advanced to map spatial variations of K and Ss between and beyond boreholes. While different methods are available, it is unclear which one yields K estimates that are most useful for high resolution predictions of groundwater flow. Therefore, the main objective of this study is to evaluate various K estimates at a highly heterogeneous field site obtained with three categories of characterization techniques including: (1) conventional methods (GSA, permeameter, and slug tests); (2) HPT surveys; and (3) inverse modeling based on geology-based zonations and highly parameterized approaches. The performance of each approach is first qualitatively analyzed by comparing K estimates to site geology. Then, steady-state and transient groundwater flow models are employed to quantitatively assess various K estimates by simulating pumping tests not used for parameter estimation. Results reveal that inverse modeling approaches yield the best drawdown predictions under both steady and transient conditions. In contrast, conventional methods and HPT surveys yield biased predictions. Based on our research, it appears that inverse modeling and data fusion are necessary steps in predicting accurate groundwater flow behavior.

Equivalent Hydraulic Conductivity of Heterogeneous Aquifers Estimated by Multi-Frequency Oscillatory Pumping

Aquifers are a vital part of our water cycle and modeling groundwater flow is paramount for a number of environmental applications. Modeling requires the estimation of aquifer properties, which is mostly achieved by field pumping tests. Oscillatory pumping tests involve repeated pumping and injection of aquifer water, which generates a periodic head signal. We investigate the estimation of equivalent hydraulic conductivity (Keq) in an oscillatory pumping test with a multi-frequency excitation. Synthetic head data are generated from numerical simulations of pumping in heterogeneous aquifers and an inverse method is applied to estimate Keq. The method involves a Fast Fourier Transform (FFT) analysis for obtaining the spatial distribution of head amplitude and then matching the amplitudes using a semi-analytical solution of a homogeneous aquifer with conductivity Keq. We investigate the dependence of Keq on pumping frequency in view of the previous literature, which has found increasing values for higher frequencies. We did not find this increase in Keq with frequency and minor variations which were observed appear to be a result of numerical error. The result implies that oscillatory pumping field tests can be used for Keq estimation without any corrections for frequency dependence.

Hydraulic Tomography Estimates Improved by Zonal Information From the Clustering of Geophysical Survey Data

AbstractHydraulic tomography (HT) has been demonstrated as a robust approach to characterize subsurface heterogeneity through the inverse modeling of multiple pumping data. However, smooth or even erroneous tomograms can result when insufficient observations are involved in the inversion. In this study, the feasibility of integrating geophysical survey data into HT analysis is investigated. First, k‐means clustering is utilized to extract zonal information from borehole geophysical logs, and a new type of spatial constraints containing geological knowledge is proposed to obtain improved hydrostratigraphic boundaries along boreholes. Next, zonation models are constructed by applying clustering‐based zone geometry and populating zonal estimates of hydraulic conductivity (K) from analyzing pumping data. Afterwards, zonation models are treated as the initial guess of spatial variability in the geostatistical inversion of HT analysis. Additionally, local K measurements can be utilized to further improve HT estimates. Comparative cases of HT analyses are designed for a numerical sandbox experiment to highlight the HT performance integrated with geophysical surveys, in which the geostatistical inversion is initialized with: (a) a homogeneous K field; (b) zonation models built by the clustering of disparate geophysical surveys with/without spatial constraints; and (c) zonation improved by incorporating local K measurements. Based on ln K field comparisons and validation through predictions of drawdowns and tracer plume migration from independent tests not used in the calibration effort, we find that integration of geophysical surveys into HT analysis by clustering with spatial constraints is demonstrated as an effective approach, and local K measurements can further improve HT estimates.

Cross-Hole and Vadose-Zone Infiltration Tracer Test Analyses to Determine Aquifer Reactive Transport Parameters at a Former Uranium Mill Site (Grand Junction, Colorado)

The U.S. Department of Energy Office of Legacy Management is responsible for the long-term care and maintenance of former uranium mill sites in the United States. Prior predictions of site flushing times (monitored natural attenuation) are not being met due to the presence of secondary contaminant sources associated with uranium-rich sediments in the vadose zone and organic-rich sediments near the water table below and near former mill tailings (tailings have been moved to a separate disposal site). Updated sitewide modeling for future releases of contaminants (including uranium) from these secondary sources to the groundwater need appropriate input parameters. To test field techniques, two cross-hole tracer tests and one infiltration tracer test were completed at a former uranium mill site in Grand Junction, Colorado. Reactive transport modeling was completed to derive physical and geochemical parameters. The observed data from saturated zone cross-hole tracer testing was adequately simulated using PHT-USG (reactive transport model) and PEST++ (calibration routine) with reasonable estimates of hydraulic conductivity, dispersion, effective porosity, cation exchange, calcite saturation index, and uranium sorption potential. The use of multiple layering in one cross-hole model was able to capture hydraulic conductivity variations with depth, which produced a double hump in the tracer concentrations. Estimated parameter values were very similar to prior estimates from column testing and single-well push–pull testing, except for a lower uranium sorption potential in one cross-hole test. This difference is likely due to the larger scale of the cross-hole testing including pathways with a lower uranium sorption potential. The infiltration testing released constituents from the vadose zone that can contribute to ongoing groundwater contamination. Modeling simulated the immediate release of these constituents to the water table similar to downward displacement of the existing residual porewater. Delayed drainage of the infiltration water was more difficult to simulate. However, the overall contaminant release concentrations from the vadose-zone secondary sources and ongoing groundwater contamination are adequately simulated for current site purposes. Additional details on vadose-zone processes may be needed if various remedial fluids are evaluated.

Estimation of hydraulic conductivity in a watershed using sparse multi-source data via Gaussian process regression and Bayesian experimental design

Enhanced water management systems depend on accurate estimation of subsurface hydraulic properties. However, geologic formations can vary significantly, so information from a single source (e.g., widely spaced boreholes) is insufficient in characterizing subsurface aquifer properties. Therefore, multiple sources of information are needed to complement the hydrogeology understanding of a region. This study presents a numerical framework in which information from different measurement sources is combined to characterize the 3D random field in a multi-fidelity prediction model. Coupled with the model, a Bayesian experimental design was used to determine the best future sampling locations. The Upper Sangamon watershed in east-central Illinois was selected as the case study site, where the multi-fidelity Gaussian process model was used to estimate the hydraulic conductivity in the region of interest. Multi-source observation data were obtained from electrical resistivity and borehole pumping tests. The accuracy of the model prediction is dependent on the locations and the distribution of both high- and low-fidelity data. Furthermore, the multi-fidelity model was compared with the single-fidelity model. The uncertainties and confidence in the measurements and parameter estimates were quantified and used to design future cycles of data collection to further improve the confidence intervals.

Comparison of saturated hydraulic conductivity estimated by empirical, hydraulic and numerical modeling methods at different scales in a coastal sand aquifer in Northern Ireland

Hydraulic conductivity is one of the most challenging hydrogeological properties to appropriately measure due to its dependence on the measurement scale and the influence of heterogeneity. This paper presents a comparison of saturated hydraulic conductivities (K) determined for a quasi-homogeneous coastal sand aquifer, estimated using eight different methodologies, encompassing empirical, hydraulic and numerical modeling methods. The geometric means of K, determined using 22 methods, spanning measurement scales varying between 0.01 and 100 m, ranged between 3.6 and 58.3 m/d. K estimates from Cone Penetration Test (CPT) data proved wider than those obtained using the other methods, while various empirical equations, commonly used to estimate K from grain-size analysis and Tide-Aquifer interaction techniques revealed variations of up to one order of magnitude. Single-well tracer dilution tests provided an alternative for making preliminary estimates of K when hydraulic gradients were known. Estimates from the slug tests proved between 1.2 and 1.6 times larger than those determined from pumping tests which, with one of the smallest ranges of variation, provided a representative average K of the aquifer as revealed by numerical modeling. By contrast, variations in K with depth could be detected at small scales (~ 0.1 m). Hydraulic Profiling Tool (HPT) system data indicated that K decreases with depth, which was supported by the numerical model results. No scale effect on K was apparent when considering the ensemble of results, suggesting that hydraulic conductivity estimates do not depend on the scale of measurement in the absence of significant aquifer heterogeneities.