High-resolution saturated hydraulic conductivity logging of borehole cores using air permeability measurements

Saturated hydraulic conductivity (K _s) is one of the most important parameters determining groundwater flow and contaminant transport in both unsaturated and saturated porous media. The hand-held air permeameter technique was investigated for high-resolution hydraulic conductivity determination on borehole cores using a spatial resolution of ～0.05 m. The suitability of such air permeameter measurements on friable to poorly indurated sediments was tested to improve the spatial prediction of classical laboratory-based K _s measurements obtained at a much lower spatial resolution (～2 m). In total, 368 K _s measurements were made on ～350 m of borehole cores originating from the Campine basin, northern Belgium, while ～5,230 air permeability measurements were performed on the same cores, resulting in a K _s range of seven orders of magnitude. Cross-validation demonstrated that, using air permeameter data as the secondary variable for laboratory based K _s measurements, the performance increased from R ²?=?0.35 for ordinary kriging (laboratory K _s only) to R ²?=?0.61 for co-kriging. The separate treatment of horizontal and vertical hydraulic conductivity revealed considerable anisotropy in certain lithostratigraphical units, while others were clearly isotropic at the sample scale. Air permeameter measurements on borehole cores provide a cost-effective way to improve spatial predictions of traditional laboratory based K _s.     

Aquifer hydraulic conductivity prediction via coupling model of MCMC-ANN

Grain-size distribution data,as a substitute for measuring hydraulic conductivity(K),has often been used to get K value indirectly.With grain-size distribution data of 150 sets of samples being input data,this study combined the Artificial Neural Network technology(ANN)and Markov Chain Monte Carlo method(MCMC),which replaced the Monte Carlo method(MC)of Generalized Likelihood Uncertainty Estimation(GLUE),to establish the GLUE-ANN model for hydraulic conductivity prediction and uncertainty analysis.By means of applying the GLUE-ANN model to a typical piedmont region and central region of North China Plain,and being compared with actually measured values of hydraulic conductivity,the relative error ranges are between 1.55%and 23.53%and between 14.08%and 27.22%respectively,the accuracy of which can meet the requirements of groundwater resources assessment.The global best parameter gained through posterior distribution test indicates that the GLUEANN model,which has satisfying sampling efficiency and optimization capability,is able to reasonably reflect the uncertainty of hydrogeological parameters.Furthermore,the influence of stochastic observation error(SOE)in grain-size analysis upon prediction of hydraulic conductivity was discussed,and it is believed that the influence can not be neglected.     

Measuring saturated hydraulic conductivity and anisotropy of peat by a modified split-container method

Long-term changes in the physical and hydraulic properties of peat result from the decomposition and consolidation processes. The saturated hydraulic conductivity (K _s) of peat is depth-dependent and could increase or decrease with depth; therefore, K _s determination on a large number of samples is required to more accurately assess field variability. The cube method is a popular laboratory procedure to determine K _s because it allows use of smaller sample dimensions, while minimizing edge effects. This article describes the design and use of an alternate split container to enclose undisturbed peat cubes during measurement of K _s and the application of this method to a field site in Northwest Territories, Canada. The method allows for simpler and more controlled K _s measurements while also permitting anisotropy measurements. Matched tests on identical samples (K _s range 21–314 m/d), using the split-container and wax methods, showed a good agreement for intermediate K _s values; however, significant deviations occurred for low and high K _s values. Hydraulic-conductivity measurements taken on field samples showed a decrease with depth and exhibited anisotropic hydraulic conductivity as expected based on previous studies.     

Measurement of streambed hydraulic conductivity and its anisotropy

 A method is described for the measurement of streambed hydraulic conductivity. Unlike permeameter methods, this method applies straight and l-shaped standpipes directly to streambeds for measurements of in-situ hydraulic conductivity in the vertical (K _v ) and horizontal (K _h ) directions, as well as in other oblique directions (K _s ). This method has advantages in determination of K _v values over grain-size analysis, permeameter tests, or slug test methods. Also unique to this method is that it provides K _s values of a streambed. The measured results can be used to construct a hydraulic conductivity ellipse and to evaluate the anisotropy of streambed sediments. Field examples from the Republican River, Nebraska, demonstrated the usefulness of this method in the determination of streambed hydraulic conductivity and anisotropy along or across a river channel. Results indicate that the K _h is about three to four times larger than K _v , whereas K _s values are larger than K _v but smaller than K _h . Received: 6 March 2000 · Accepted: 18 April 2000     

Uncertainty of rainfall-induced landslides considering spatial variability of parameters

A cross-correlation analysis is conducted to determine the impacts of the heterogeneity of hydraulic conductivity K_s, soil cohesion c′ and soil friction angle (tan φ′) on the uncertainty of slope stability in time and space during rainfall. We find the relative importance of tan φ′ and c′ depends on the effective stress. While the sensitivity of the stability to the variability of K_s is small, the large coefficient of variation of K_s may exacerbate the variability of pore-water pressure. Therefore, characterizing the heterogeneity of hydraulic properties and pore-water distribution in the field is critical to the stability analysis.     

Estimating regional-scale fractured bedrock hydraulic conductivity using discrete fracture network (DFN) modeling

Estimating bedrock hydraulic conductivity of regional fractured aquifers is challenging due to a lack of aquifer testing data and the presence of small and large-scale heterogeneity. This study provides a novel approach for estimating the bedrock hydraulic conductivity of a regional-scale fractured bedrock aquifer using discrete fracture network (DFN) modeling. The methodology is tested in the mountainous Okanagan Basin, British Columbia, Canada. Discrete fractures were mapped in outcrops, and larger-scale fracture zones (corresponding to lineaments) were mapped from orthophotos and LANDSAT imagery. Outcrop fracture data were used to generate DFN models for estimating hydraulic conductivity for the fractured matrix (K _m). The mountain block hydraulic conductivity (K _mb) was estimated using larger-scale DFN models. Lineament properties were estimated by best fit parameters for a simulated pumping test influenced by a fracture zone. Unknown dip angles and directions for lineaments were estimated from the small-scale fracture sets. Simulated K _m and K _mb values range from 10^–8 to 10^–7?m/s and are greatest in a N–S direction, coinciding with the main strike direction of Okanagan Valley Fault Zone. K _mb values also decrease away from the fault, consistent with the decrease in lineament density. Simulated hydraulic conductivity values compare well with those estimated from pumping tests.     

Estimating hydraulic conductivity using grain-size analyses, aquifer tests, and numerical modeling in a riverside alluvial system in South Korea

Hydraulic conductivity (K) for an alluvial system in a riverbank filtration area in Changwon City, South Korea, has been studied using grain-size distribution, pumping and slug tests, and numerical modeling. The alluvial system is composed of layers: upper fine sand, medium sand, lower fine sand, and a highly conductive sand/gravel layer at the base. The geometric mean of K for the sand/gravel layer (9.89?×?10^?4 m s^?1), as determined by grain-size analyses, was 3.33 times greater than the geometric mean obtained from pumping tests (2.97?×?10^?4 m s^?1). The geometric mean of K estimates obtained from slug tests (3.08?×?10^?6 m s^?1) was one to two orders of magnitude lower than that from pumping tests and grain-size analyses. K estimates derived from a numerical model were compared to those derived from the grain-size methods, slug tests and pumping tests in order to determine the degree of deviation from the numerical model. It is considered that the K estimates determined by the slug tests resemble the uppermost part of the alluvial deposit, whereas the K estimates obtained by grain-size analyses and pumping tests are similar to those from the numerical model for the sand/gravel layer of the riverside alluvial system.     

Hydraulic conductivity of shallow sandy aquifers: Effects of sedimentologic and diagenetic differences

The relationship between values of hydraulic conductivity determined from grain-size methods,K _gr, and those from pumping-test methods,K _pt, have been evaluated statistically using data from recent and older sandy materials. It is shown that both methods generally give the same values only in recent sediments that have not been subjected to significant diagenetic alteration and give variable results in older and diagenetically altered sediments. The ratioK _pt/K_gr appears to vary, probably in response to the degree of diagenetic alteration. It is further found that methods incorporating the effects of grain size (d1O or d50) and sorting could give betterK values than those incorporating the effect of grain-size only and, thus, suggest the joint inclusion of both parameters in the grain-size determination of permeability.     

Hydraulic tomography analysis of municipal-well operation data with geology-based groundwater models

The sustainable management of groundwater resources is essential to municipalities worldwide due to increasing water demand. Planning for the optimized use of groundwater resources requires reliable estimation of hydraulic parameters such as hydraulic conductivity (K) and specific storage (S_s). However, estimation of hydraulic parameters can be difficult with dedicated pumping tests while municipal wells are in operation. In this study, the K and S_s of a highly heterogeneous, multi-aquifer/aquitard system are estimated through the inverse modeling of water-level data from observation wells collected during municipal well operations. In particular, four different geological models are calibrated by coupling HydroGeoSphere (HGS) with the parameter estimation code PEST. The joint analysis of water-level records resulting from fluctuating pumping and injection operations amounts to a hydraulic tomography (HT) analysis. The four geological models are well calibrated and yield reliable estimates that are consistent with previously studies. Overall, this research reveals that: (1) the HT analysis of municipal well records is feasible and yields reliable K and S_s estimates for individual geological units where drawdown records are available; (2) these estimates are obtained at the scale of intended use, unlike small-scale estimates typically obtained through other characterization methods; (3) the HT analysis can be conducted using existing data, which leads to substantial cost savings; and (4) data collected during municipal well operations can be used in the development of new groundwater models or in the calibration of existing groundwater models, thus they are valuable and should be archived.

     

Estimating the hydraulic properties of an aquitard from in situ pore pressure measurements

A workflow is described to estimate specific storage (S _s) and hydraulic conductivity (K) from a profile of vibrating wire piezometers embedded into a regional aquitard in Australia. The loading efficiency, compressibility and S _s were estimated from pore pressure response to atmospheric pressure changes, and K was estimated from the earliest part of the measurement record following grouting. Results indicate that S _s and K were, respectively, 8.8?×?10^?6 to 1.2?×?10^?5 m^?1 and 2?×?10^?12 m s^?1 for a claystone/siltstone, and 4.3?×?10^?6 to 9.6?×?10^?6 m^?1 and 1?×?10^?12 to 5?×?10^?12 m s^?1 for a thick mudstone. K estimates from the pore pressure response are within one order of magnitude when compared to direct measurement in a laboratory and inverse modelled flux rates determined from natural tracer profiles. Further analysis of the evolution and longevity of the properties of borehole grout (e.g. thermal and chemical effects) may help refine the estimation of formation hydraulic properties using this workflow. However, the convergence of K values illustrates the benefit of multiple lines of evidence to support aquitard characterization. An additional benefit of in situ pore pressure measurement is the generation of long-term data to constrain groundwater flow models, which provides a link between laboratory scale data and the formation scale.     

Improved barometric and loading efficiency estimates using packers in monitoring wells

Measurement of barometric efficiency (BE) from open monitoring wells or loading efficiency (LE) from formation pore pressures provides valuable information about the hydraulic properties and confinement of a formation. Drained compressibility (α) can be calculated from LE (or BE) in confined and semi-confined formations and used to calculate specific storage (S _s). S _s and α are important for predicting the effects of groundwater extraction and therefore for sustainable extraction management. However, in low hydraulic conductivity (K) formations or large diameter monitoring wells, time lags caused by well storage may be so long that BE cannot be properly assessed in open monitoring wells in confined or unconfined settings. This study demonstrates the use of packers to reduce monitoring-well time lags and enable reliable assessments of LE. In one example from a confined, high-K formation, estimates of BE in the open monitoring well were in good agreement with shut-in LE estimates. In a second example, from a low-K confining clay layer, BE could not be adequately assessed in the open monitoring well due to time lag. Sealing the monitoring well with a packer reduced the time lag sufficiently that a reliable assessment of LE could be made from a 24-day monitoring period. The shut-in response confirmed confined conditions at the well screen and provided confidence in the assessment of hydraulic parameters. A short (time-lag-dependent) period of high-frequency shut-in monitoring can therefore enhance understanding of hydrogeological systems and potentially provide hydraulic parameters to improve conceptual/numerical groundwater models.     

Pile Head Cyclic Lateral Loading of Single Pile

This paper presents an elastic continuum model using an extended nonlinear Davies and Budhu equations, which enables the nonlinear behavior of the soil around the long elastic pile to be modeled using a simple expression of pile-head stiffness method. The calculated results were validated with the measured full-scale dynamic field tests data conducted in Auckland residual clay. An idealized soil profile and soil stiffness under small strain (i.e. shear modulus, G _s and shear wave velocity, V _s of the soil) determined from in situ testing was used to model the single pile tests results. The predictions of these extended equations are also confirmed by using the three-dimensional finite-element OpenSeesPL (Lu et al. in OpenSeesPL 3D lateral pile-ground interaction: user manual, University of California, San Diego, 2010). A soil stiffness reduction factor, G _s /G _s,max of 0.36 was introduced to the proposed method and model. It was found to give a reasonable prediction for a single pile subjected to dynamic lateral loading. The reduction in soil stiffness found from the experiment arises from the cumulative effects of pile–soil separation as well as a change in the soil properties subjected to cyclic load. In summary, if the proposed method and model are accurately verified and properly used, then they are capable of producing realistic predictions. Both models provide good modelling tools to replicate the full-scale dynamic test results.     

Heterogeneity of hydraulic conductivity and Darcian flux in the submerged streambed and adjacent exposed stream bank of the Beiluo River,northwest China

Recognizing the heterogeneity of hydraulic conductivity and hyporheic flow is critical for understanding contaminant transfer and biogeochemical and hydrological processes involving streams and aquifers. In this study, the heterogeneity of hydraulic conductivity and Darcian flux in a submerged streambed and its adjacent exposed stream banks were investigated in the Beiluo River, northwest China. In the submerged streambed, Darcian flux was estimated by measurement of vertical hydraulic conductivity (K _v) and vertical head gradient (VHG) using in-situ permeameter tests. On exposed stream banks, both horizontal hydraulic conductivity (K _h) and K _v were measured by on-site permeameter tests. In the submerged streambed, K _v values gradually decreased with depth and the higher values were concentrated in the center and close to the erosional bank. Compared to the exposed stream banks, the K _v values were higher in the streambed. From stream stage to the topmost layer of tested sediment, through increasing elevation, the K _h values increased on the erosional bank, while they decreased on the depositional bank. The values of VHG along the thalweg illustrate that downwelling flux occurred in the deepest area while upwelling flux appeared in the other areas, which might result from the change of streambed elevation. The higher value of the Darcian flux in the submerged streambed existed near the erosional bank.     

Impact of pore water conductivity and porosity on the electrical conductivity of kaolinite

The purpose of this study is to quantify the magnitudes of surface conduction and pore water conduction from the measured electrical conductivity of kaolinite, with the ultimate goal of estimating the electrical conductivity of kaolinite with a wide range of pore water conductivities (σ _w = 0.013–3.356 S/m) and porosities (n = 0.368–1.0). Therefore, the theoretical background of the electrical conductivity in soils was reviewed, and electrical conductivity measurements on kaolinite were performed using both slurry and consolidation tests in this study. The results of this study demonstrate that the variations of measured electrical conductivity (σ _mix) with n are debatable according to the values of σ _w, because a decrease in n results in both an increase in surface conduction (K _s) and a decrease in pore water conduction (K _w); this causes the relative magnitude of K _s compared to that of K _w to vary with σ _w and n. Consequently, this study develops the relation between the porosity-normalized K _s/K _w and 1/σ _w. Additionally, the surface conductivity of the tested kaolinite is back-calculated and compared with the previous relationship between K _s and zeta potential of kaolinite. The measured and estimated σ _mix values are compared with the varying pore water conductivity and porosity values.     

Numerical modeling of contaminant transport in a stratified heterogeneous aquifer with dipping anisotropy

The combined influence of dip angle and adsorption heterogeneity on solute transport mechanisms in heterogeneous media can be understood by performing simulations of steady-state flow and transient transport in a heterogeneous aquifer with dipping anisotropy. Reactive and non-reactive contaminant transport in various types of heterogeneous aquifer is studied by simulations. The hydraulic conductivity (K) of the heterogeneous aquifer is generated by HYDRO_GEN with a Gaussian correlation spectrum. By considering the heterogeneity of the adsorption distribution coefficient (K _d), a perfect negative correlation between lnK and lnK _d is obtained by using the spherical grains model. The generated K and K _d are used as input to groundwater flow and transport models to investigate the effects of dipping sedimentary heterogeneity on contaminant plume evolution. Simulation results showed that the magnitude of the dip angle strongly controls the plume evolution in the studied anisotropic and heterogeneous aquifer. The retarded average pore-water velocity (v/R) of the adsorption model significantly controls the horizontal spreading of the plume. The bottom plume is intensively retarded in the zones between the dipping lenses of lower hydraulic conductivity and the no-flow bottom boundary. The implications of these findings are very important for the management of contaminated heterogeneous aquifers.     

Hydrostratigraphy and geochemistry at a coastal sandfill in Singapore

A characterization study was carried out in a 10-m-thick sandfill, formed by hydraulic filling with marine sand, in Singapore. Placement methods and compaction were found to influence hydrostratigraphy. The deepest part of the sandfill consists of a loose sand layer and is overlain by a medium sand layer extending to mean sea level (MSL). At certain locations, a thin silty-sand layer was found. The different layers within the saturated zone were found to have different values for hydraulic conductivity (K) and groundwater flow velocity. Estimates for K increase according to the following sequence of methods: repacked sand column, step-pumping test, grain-size analysis and slug test. Slug tests and grain-size analysis yielded comparable estimates of K. The freshwater lens in the older part of the sandfill is about 2 m thicker than in a recently completed area. Comparisons of Ca²⁺/Cl^?, Mg²⁺/Cl^?, K ⁺/Cl^? and \({\text{Cl}}^{{\text{ - }}} {\text{/}}{\left( {{\text{Cl}}^{{\text{ - }}} {\text{ + HCO}}^{{\text{ - }}}_{{\text{3}}} } \right)}\) ratios indicate that the chemical composition of the groundwater at shallower depths has probably been altered by mineral dissolution. Weathering of carbonate minerals was found to be a major contributor to the major ions at these depths. The molar ratios approach the value for seawater at greater depths. The groundwater is close to equilibrium with calcite.     

Uncertainty analysis in bed load transport prediction of gravel bed rivers by ANN and ANFIS

River flow is a complex dynamic system of hydraulic and sediment transport. Bed load transport have a dynamic nature in gravel bed rivers and because of the complexity of the phenomenon include uncertainties in predictions. In the present paper, two methods based on the Artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are developed by using 360 data points. Totally, 21 different combination of input parameters are used for predicting bed load transport in gravel bed rivers. In order to acquire reliable data subsets of training and testing, subset selection of maximum dissimilarity (SSMD) method, rather than classical trial and error method, is used in finding randomly manipulation of these subsets. Furthermore, uncertainty analysis of ANN and ANFIS models are determined using Monte Carlo simulation. Two uncertainty indices of d factor and 95% prediction uncertainty and uncertainty bounds in comparison with observed values show that these models have relatively large uncertainties in bed load predictions and using of them in practical problems requires considerable effort on training and developing processes. Results indicated that ANFIS and ANN are suitable models for predicting bed load transport; but there are many uncertainties in determination of bed load transport by ANFIS and ANN, especially for high sediment loads. Based on the predictions and confidence intervals, the superiority of ANFIS to those of ANN is proved.     

Predicting saturated hydraulic conductivity using particle swarm optimization and genetic algorithm

Soil saturated hydraulic conductivity (K_s) is considered as soil basic hydraulic property, and its precision estimation is a key element in modeling water flow and solute transport processes both in the saturated and vadose zones. Although some predictive methods (e.g., pedotransfer functions, PTFs) have been proposed to indirectly predict K_s, the accuracy of these methods still needs to be improved. In this study, some easily available soil properties (e.g., particle size distribution, organic carbon, calcium carbonate content, electrical conductivity, and soil bulk density) are employed as input variables to predict K_s using a fuzzy inference system (FIS) trained by two different optimization techniques: particle swarm optimization (PSO) and genetic algorithm (GA). To verify the derived FIS, 113 soil samples were taken, and their required physical properties were measured (113 sample points?×?7 factors?=?791 input data). The initial FIS is compared with two methods: FIS trained by PSO (PSO-FIS) and FIS trained by GA (GA-FIS). Based on experimental results, all three methods are compared according to some evaluation criteria including correlation coefficient (r), modeling efficiency (EF), coefficient of determination (CD), root mean square error (RMSE), and maximum error (ME) statistics. The results showed that the PSO-FIS model achieved a higher level of modeling efficiency and coefficient of determination (R²) in comparison with the initial FIS and the GA-FIS model. EF and R² values obtained by the developed PSO-FIS model were 0.69 and 0.72, whereas they were 0.63 and 0.54 for the GA-FIS model. Moreover, the results of ME and RMSE indices showed that the PSO-FIS model can estimate soil saturated hydraulic conductivity more accurate than the GA-FIS model with ME?=?10.4 versus 11.5 and RMSE?=?5.2 versus 5.5 for PSO-FIS and GA-FIS, respectively.     

Expériences de drainage et estimation de paramètres en milieu poreux non saturé

Two drainage experiments were carried out to identify the hydraulic parameters of a quartz sand by using inverse modelling. Our results, based on sensitivity and error analyses, show that (i) water content measurements are essential to estimate the parameters of the van Genuchten–Mualem retention curve, (ii) the saturated conductivity ($K_{\mathrm{s}}$) has a low sensitivity to pressure data and is not sensitive to other measures (water content and measured outflow), (iii) the inverse approach can be verified by comparing the simulated outflow to the measured one. To cite this article: H. Beydoun, F. Lehmann, C. R. Geoscience 338 (2006).     

Hydraulic conductivity prediction based on grain-size distribution using M5 model tree

The hydraulic conductivity, K_s, is one of the most important hydraulic properties which controls the water and solute movement into the soil. It is measured on soil specimens in the laboratory. On the other hand, sometimes it is obtained by tests carried out in the field by a number of researchers. Therefore, several experimental formulas have developed to predict it. Recently, soft computing tools have been used to evaluate the hydraulic conductivity. However, these tools are not as transparent as empirical formulas. In this study, another soft computing approach, i.e. model trees, have been used for predicting the hydraulic conductivity. The main advantage of model trees is that, unlike the other data learning tools, they are easier to use and represent understandable mathematical rules more clearly. In this paper, a new formula that includes some parameters is derived to estimate the hydraulic conductivity. To develop the new formulas, experimental data sets of hydraulic conductivity were used. A comparison is made between the estimated hydraulic conductivity by this new formula and formulas given by other’s researches.