Soil water content and salinity determination using different dielectric methods in saline gypsiferous soil / Détermination de la teneur en eau et de la salinité de sols salins gypseux à l'aide de différentes méthodes diélectriques

Abstract

Measurements of dielectric permittivity and electrical conductivity were taken in a saline gypsiferous soil collected from southern Tunisia. Both time domain reflectometry (TDR) and the new WET sensor based on frequency domain reflectometry (FDR) were used. Seven different moistening solutions were used with electrical conductivities of 0.0053–14 dS m^?1. Different models for describing the observed relationships between dielectric permittivity (K _a ) and water content (θ), and bulk electrical conductivity (EC _a ) and pore water electrical conductivity (EC _p ) were tested and evaluated. The commonly used K _a –θ models by Topp et al. (1980) and Ledieu et al. (1986) cannot be recommended for the WET sensor. With these models, the RMSE and the mean relative error of the predicted θ were about 0.04 m³ m^?3 and 19% for TDR and 0.08 m³ m^?3 and 54% for WET sensor measurements, respectively. Using the Hilhorst (2000) model for EC _p predictions, the RMSE was 1.16 dS m^?1 and 4.15 dS m^?1 using TDR and the WET sensor, respectively. The WET sensor could give similar accuracy to TDR if calibrated values of the soil parameter were used instead of standard values.     

Estimation of LNAPL saturation in fine sand using time-domain reflectometry / Estimation de la saturation en LPNAL dans du sable fin grâce à la réflectométrie en domaine temporel

Abstract

Abstract Recently, substantial progress has been made in detection and observation of non-aqueous phase liquids (NAPLs) in the subsurface using different experimental techniques. However, there is still a lack of appropriate direct methods to measure the saturation of NAPL (θ_NAPL). This paper provides a guide for estimating θ_NAPL and water content (θ _w ) in unsaturated and saturated sand based on direct measurements of soil dielectric constant (K_a ) and electrical conductivity (σ _a ) using time domain reflectometry (TDR). The results show that the previously used dielectric mixing model fails to predict θ_NAPL in the case of a four-phase system. A new methodology is suggested and exemplified by showing that the measured K_a gives accurate estimation of θ_NAPL for a three-phase system while in a four-phase system, both θ _a and K_a need to be measured. The results show that using the suggested methodology, accurate predictions of θ _w (R ² = 0.9998) and θ_NAPL lower than 0.20 m³ m^-3 (average R ² = 0.9756) are possible.     

TDR measurement of stem and soil water content in two Mediterranean oak species

Abstract

Since 1990s, time domain reflectometry (TDR) has been applied to estimate the stem water content of living trees. Here, new calibration equations relating the apparent dielectric constant (K_a ) to the volumetric water content (θ) were developed for two Mediterranean oak species. Our calibration equations differ from those previously calculated for other species, suggesting that stem water contents could be monitored more accurately using species-specific curves. The stem water content in the trees of these species and the surrounding soil were monitored with TDR to examine the feasibility of this technology for recording changes in trunk water storage. The average stem water contents of the oaks reflect the soil water contents, and the temporal differences observed (17%) point to the importance of trunk water for coping with soil water deficit. Although it would be very useful to obtain a single function to estimate the stem water content of trees, it remains necessary to obtain the results in more species.     

Time domain reflectometry measurement principles and applications

Time domain reflectometry (TDR) is a highly accurate and automatable method for determination of porous media water content and electrical conductivity. Water content is inferred from the dielectric permittivity of the medium, whereas electrical conductivity is inferred from TDR signal attenuation. Empirical and dielectric mixing models are used to relate water content to measured dielectric permittivity. Clay and organic matter bind substantial amounts of water, such that measured bulk dielectric constant is reduced and the relationship with total water content requires individual calibration. A variety of TDR probe configurations provide users with site‐ and media‐specific options. Advances in TDR technology and in other dielectric methods offer the promise not only for less expensive and more accurate tools for electrical determination of water and solute contents, but also a host of other properties such as specific surface area, and retention properties of porous media. Copyright © 2002 John Wiley & Sons, Ltd.     

An advanced process for evaluating a linear dielectric constant–bulk electrical conductivity model using a capacitance sensor in field conditions

Abstract

The Hilhorst model was used to convert bulk electrical conductivity (σ_b) to pore water electrical conductivity (σ_p) under laboratory conditions by using the linear relationship between the soil dielectric constant (ε_b) and σ_b. In the present study, applying the linear relationship ε_b–σ_b to data obtained from field capacitance sensors resulted in strong positive autocorrelations between the residuals of that regression. We were able to derive an accurate offset of the relationship ε_b–σ_b and to estimate the evolution of σ_p over time by including a stochastic component to the linear model, rearranging it to a time-varying dynamic linear model (DLM), and using Kalman filtering and smoothing. The offset proved to vary for each depth in the same soil profile. A reason for this might be the changes in soil temperature along the soil profile.

Editor D. Koutsoyiannis; Associate editor M.D. Fidelibus     

TDR pressure cell for monitoring water content retention and bulk electrical conductivity curves in undisturbed soil samples

The water retention curve (θ(ψ)), which defines the relationship between soil volumetric water content (θ) and matric potential (ψ), is of paramount importance in characterizing the hydraulic behaviour of soils. However, few methods are so far available for estimating θ(ψ) in undisturbed soil samples. We present a new design of TDR‐pressure cell (TDR‐Cell) for estimating θ(ψ) in undisturbed soil samples. The TDR‐Cell consists of a 50‐mm‐long and 50‐mm internal diameter stainless steel cylinder (which constitutes the outer frame of a coaxial line) attached to a porous ceramic disc and closed at the ends with two aluminium lids. A 49‐mm‐long and 3‐mm‐diameter stainless steel rod, which runs longitudinally through the centre of the cylinder, constitutes the inner rod of a coaxial TDR probe. The TDR‐Cell was used to determine the θ(ψ) curves of a packed sand and seven undisturbed soil samples from three profiles of agricultural soils. These θ(ψ) curves were subsequently compared to those obtained from the corresponding 2‐mm sieved soils using the pressure plate method. Measurements of bulk electrical conductivity, σ_a, as a function of the water content, σ_a(θ), of the undisturbed soil samples were also performed. An excellent correlation (R² = 0·988) was found between the θ values measured by TDR on the different undisturbed soils and the corresponding θ obtained from the soil gravimetric water content. A typical bimodal θ(ψ) function was found for most of the undisturbed soil samples. Comparison between the θ(ψ) curves measured with the TDR‐Cell and those obtained from the 2‐mm sieved soils showed that the pressure plate method overestimates θ at low ψ values. The σ_a(θ) relationship was well described by a simple power expression (R² > 0·95), in which the power factor, defined as tortuosity, ranged between 1·18 and 3·75. Copyright © 2011 John Wiley & Sons, Ltd.     

A dripper-TDR method for in situ determination of hydraulic conductivity and chemical transport properties of surface soils

Field determined hydraulic and chemical transport properties can be useful for the protection of groundwater resources from land-applied chemicals. Most field methods to determine flow and transport parameters are either time or energy consuming and/or they provide a single measurement for a given time period. In this study, we present a dripper-TDR field method that allows measurement of hydraulic conductivity and chemical transport parameters at multiple field locations within a short time period. Specifically, the dripper-TDR determines saturated hydraulic conductivity (K_s), macroscopic capillary length (λ_c), immobile water fraction (θ_im/θ), mass exchange coefficient (α) and dispersion coefficient (D_m). Multiple dripper lines were positioned over five crop rows in a field. Background and step solutions were applied through drippers to determine surface hydraulic conductivity parameters at 44 locations and surface transport properties at 38 locations. The hydraulic conductivity parameters (K_s, λ_c) were determined by application of three discharge rates from the drippers and measurements of the resultant steady-state flux densities at the soil surface beneath each dripper. Time domain reflectometry (TDR) was used to measure the bulk electrical conductivity of the soil during steady infiltration of a salt solution. Breakthrough curves (BTCs) for all sites were determined from the TDR measurements. The K_s and λ_c values were found to be lognormally distributed with average values of 31.4 cm h⁻¹ and 6.0 cm, respectively. BTC analysis produced chemical properties, θ_im/θ, α, and D_m with average values of 0.23, 0.0036 h⁻¹, and 1220 cm² h⁻¹, respectively. The estimated values of the flow and transport parameters were found to be within the ranges of values reported by previous studies conducted at nearby field locations. The dripper TDR method is a rapid and useful technique for in situ measurements of hydraulic conductivity and solute transport properties. The measurements reported in this study give clear evidence to the occurrence of non-equilibrium water and chemical movement in surface soil. The method allows for quantification of non-equilibrium model parameters and preferential flow. Quantifying the parameters is a necessary step toward determining the influences of surface properties on infiltration, runoff, and vadose zone transport.     

Effect of anisotropy on solute transport in degraded fen peat soils

Peat soils are heterogeneous, anisotropic porous media. Compared to mineral soils, there is still limited understanding of physical and solute transport properties of fen peat soils. In this study, we aimed to explore the effect of soil anisotropy on solute transport in degraded fen peat. Undisturbed soil cores, taken in vertical and horizontal direction, were collected from one drained and one restored fen peatland both in a comparable state of soil degradation. Saturated hydraulic conductivity (K _s) and chemical properties of peat were determined for all soil cores. Miscible displacement experiments were conducted under saturated steady state conditions using potassium bromide as a conservative tracer. The results showed that (1) the K _s in vertical direction (K _sv) was significantly higher than that in horizontal direction (K_sh), indicating that K _s of degraded fen peat behaves anisotropically; (2) pronounced preferential flow occurred in vertical direction with a higher immobile water fraction and a higher pore water velocity; (3) the 5% arrival time (a proxy for the strength of preferential flow) was affected by soil anisotropy as well as study site. A strong correlation was found between 5% arrival time and dispersivity, K _s and mobile water fraction; (4) phosphate release was observed from drained peat only. The impact of soil heterogeneity on phosphate leaching was more pronounced than soil anisotropy. The soil core with the strongest preferential flow released the highest amount of phosphate. We conclude that soil anisotropy is crucial in peatland hydrology but additional research is required to fully understand anisotropy effects on solute transport.     

Electromagnetic induction prediction of soil salinity and groundwater properties in a Tunisian Saharan oasis

Abstract

Electromagnetic induction measurements (EM) were taken in a saline gypsiferous soil of the Saharan-climate Fatnassa oasis (Tunisia) to predict the electrical conductivity of saturated soil extract (ECe) and shallow groundwater properties (depth, Dgw, and electrical conductivity, ECgw) using various models. The soil profile was sampled at 0.2 m depth intervals to 1.2 m for physical and chemical analysis. The best input to predict the log-transformed soil salinity (lnECe) in surface (0–0.2 m) soil was the EMh/EMv ratio. For the 0–0.6 m soil depth interval, the performance of multiple linear regression (MLR) models to predict lnECe was weaker using data collected over various seasons and years (R _a ² = 0.66 and MSE = 0.083 dS m^-1) as compared to those collected during the same period (R _a ² = 0.97, MSE = 0.007 dS m^-1). For similar seasonal conditions, for the Dgw–EMv relationship, R ² was 0.88 and the MSE was 0.02 m for Dgw prediction. For a validation subset, the R ² was 0.85 and the MSE was 0.03 m. Soil salinity was predicted more accurately when groundwater properties were used instead of soil moisture with EM variables as input in the MLR.

Editor D. Koutsoyiannis; Associate editor K. Heal

Citation Bouksila, F., Persson, M., Bahri, A., and Berndtsson, R., 2012. Electromagnetic induction predictions of soil salinity and groundwater properties in a Tunisian Saharan oasis. Hydrological Sciences Journal, 57 (7), 1473–1486.     

FIELD ESTIMATION OF MACROPORE FUNCTIONING AND SURFACE HYDRAULIC CONDUCTIVITY IN A FEN PEAT

A limitation of existing models of water and solute movement in fen peats is that they fail to represent processes in the unsaturated zone. This limitation is largely due to a lack of data on the hydraulic properties of unsaturated peat, in particular the relationship between hydraulic conductivity (K) and pressure head (ψ). A tension infiltrometer was used to measure K(ψ) of a fen peat in Somerset, England. It was found that macropores could be important in water and solute movement in this soil type. It was also found that (i) variability of K in this peat was less than that reported for other peats and mineral soils, and (ii) the K data were better described by a log-normal distribution than a normal distribution in accord with findings from other peat and mineral soils. Recommendations on improving the understanding of water and solute movement in the unsaturated zone of this soil type are made. © 1997 by John Wiley & Sons, Ltd.     

Inversion of multi-frequency electromagnetic induction data for 3D characterization of hydraulic conductivity

Electromagnetic induction (EMI) instruments provide rapid, noninvasive, and spatially dense data for characterization of soil and groundwater properties. Data from multi-frequency EMI tools can be inverted to provide quantitative electrical conductivity estimates as a function of depth. In this study, multi-frequency EMI data collected across an abandoned uranium mill site near Naturita, Colorado, USA, are inverted to produce vertical distribution of electrical conductivity (EC) across the site. The relation between measured apparent electrical conductivity (EC_a) and hydraulic conductivity (K) is weak (correlation coefficient of 0.20), whereas the correlation between the depth dependent EC obtained from the inversions, and K is sufficiently strong to be used for hydrologic estimation (correlation coefficient of ? 0.62). Depth-specific EC values were correlated with co-located K measurements to develop a site-specific ln(EC)–ln(K) relation. This petrophysical relation was applied to produce a spatially detailed map of K across the study area. A synthetic example based on EC_a values at the site was used to assess model resolution and correlation loss given variations in depth and/or measurement error. Results from synthetic modeling indicate that optimum correlation with K occurs at ~ 0.5 m followed by a gradual correlation loss of 90% at 2.3 m. These results are consistent with an analysis of depth of investigation (DOI) given the range of frequencies, transmitter–receiver separation, and measurement errors for the field data. DOIs were estimated at 2.0 ± 0.5 m depending on the soil conductivities. A 4-layer model, with varying thicknesses, was used to invert the EC_a to maximize available information within the aquifer region for improved correlations with K. Results show improved correlation between K and the corresponding inverted EC at similar depths, underscoring the importance of inversion in using multi-frequency EMI data for hydrologic estimation.     

An efficient tool for accelerating the numerical solution of the stochastic subsurface flow problem using neural networks

 An efficient numerical solution for the two-dimensional groundwater flow problem using artificial neural networks (ANNs) is presented. Under stationary velocity conditions with unidirectional mean flow, the conductivity realizations and the head gradients, obtained by a traditional finite difference solution to the flow equation, are given as input-output pairs to train a neural network. The ANN is trained successfully and a certain level of recognition of the relationship between input conductivity patterns and output head gradients is achieved. The trained network produced velocity realizations that are physically plausible without solving the flow equation for each of the conductivity realizations. This is achieved in a small fraction of the time necessary for solving the flow equations. The prediction accuracy of the ANN reaches 97.5% for the longitudinal head gradient and 94.7% for the transverse gradient. Head-gradient and velocity statistics in terms of the first two moments are obtained with a very high accuracy. The cross covariances between head gradients and the fluctuating log-conductivity (log-K) and between velocity and log-K obtained with the ANN approach match very closely those obtained by a traditional numerical solution. The same is true for the velocity components auto-covariances. The results are also extended to transport simulations with very good accuracy. Spatial moments (up to the fourth) of mean-concentration plumes obtained using ANNs are in very good agreement with the traditional Monte Carlo simulations. Furthermore, the concentration second moment (concentration variance) is very close between the two approaches. Considering the fact that higher moments of concentration need more computational effort in numerical simulations, the advantage of the presented approach in saving long computational times is evident. Another advantage of the ANNs approach is the ability to generalize a trained network to conductivity distributions different from those used in training. However, the accuracy of the approach in cases with higher conductivity variances is being investigated.     

Identification of optimal soil hydraulic functions and parameters for predicting soil moisture

Abstract

The accuracy of six combined methods formed by three commonly-used soil hydraulic functions and two methods to determine soil hydraulic parameters based on a soil hydraulic parameter look-up table and soil pedotransfer functions was examined for simulating soil moisture. A novel data analysis and modelling approach was used that eliminated the effects of evapotranspiration so that specific sources of error among the six combined methods could be identified and quantified. By comparing simulated and observed soil moisture at six sites of the USDA Soil Climate Analysis Network, we identified the optimal soil hydraulic functions and parameters for predicting soil moisture. Through sensitivity tests, we also showed that adjusting only the soil saturated hydraulic conductivity, K_s , is insufficient for representing important effects of macropores on soil hydraulic conductivity. Our analysis illustrates that, in general, soil hydraulic conductivity is less sensitive to K_s than to the soil pore-size distribution parameter.

Editor D. Koutsoyiannis; Associate editor D. Hughes

Citation Pan, F., McKane, R.B. and Stieglitz, M., 2012. Identification of optimal soil hydraulic functions and parameters for predicting soil moisture. Hydrological Sciences Journal, 57 (4), 723–737.     

Runoff modelling at two field slopes: use of in situ measurements of air permeability to characterize spatial variability of saturated hydraulic conductivity

The time required at a field site to obtain a few measurements of saturated hydraulic conductivity (K_s) will allow for many measurements of soil air permeability (k_a). This study investigates if k_a measured in situ (k_{a, in situ}) can be a substitute for measurement of K_s in relation to infiltration and surface runoff modelling. Measurements of k_{a, in situ} were carried out in two small agricultural catchments. A spatial correlation of the log‐transformed values existed having a range of approximately 100 m. A predictive relationship between K_s and k_a measured on 100‐cm³ soil samples in the laboratory was derived for one of the field slopes and showed good agreement with an earlier suggested predictive K_s–k_a relationship. In situ measurements of K_s and k_a suggested that the predictive relationships also could be used at larger scale. The K_s–k_a relationships together with the k_{a, in situ} data were applied in a distributed surface runoff (DSR) model, simulating a high‐intensity rainfall event. The DSR simulation results were highly dependent on whether the geometric average of k_{a, in situ} or kriged values of k_{a, in situ} was used as model input. When increasing the resolution of K_s in the DSR model, a limit of 30–40 m was found for both field slopes. Below this limit, the simulated runoff and hydrograph peaks were independent of resolution scale. If only a few randomly chosen values of K_s were used to represent the spatial variation within the field slope, very large deviations in repeated DSR simulation results were obtained, both with respect to peak height and hydrograph shape. In contrast, when using many predicted K_s values based on a K_s–k_a relationship and measured k_{a, in situ} data, the DSR model generally captured the correct hydrograph shape although simulations were sensitive to the chosen K_s–k_a relationship. As massive measurement efforts normally will be required to obtain a satisfactory representation of the spatial variability in K_s, the use of k_{a, in situ} to assess spatial variability in K_s appears a promising alternative. Copyright © 2004 John Wiley & Sons, Ltd.     

Impact of fractional probability distributions on statistics of hydraulic conductivity,dynamics of groundwater flow and solute transport at a low-permeability site

The fractional Brownian motion (fBm) and fractional Lévy motion (fLm) can easily describe the geometry and the statistical structure of hydraulic conductivity (K) for real-world. However, the fBm and fLm models have not been systematically evaluated when building the K field for a low-permeability site. In this study, both the fBm and fLm are used to simulate the low-K field at NingCheGu (NCG), Tianjin, China. Groundwater flow and solute transport are then computed using MODFLOW and MT3DMS, respectively, and the influence of the fBm/fLm models for K on groundwater flow and solute transport is discussed. Results show that the fLm fits better the statistics of the low-K medium than fBm, and the random logarithmic K (LnK) field generated by fLm is more stable because the resultant LnK field captures more of the measured properties at the field site than that generated by fBm. In contrast, the LnK generated by fBm is more likely to form both high-K channels and low-K barriers. The fBm therefore predicts more extreme behaviours in flow and transport, including the preferential flow, low-concentration blocks and solute retention. The overall groundwater renewal period and solute travel time for the fLm simulation are slightly shorter than those for fBm. The impacts of the fLm and fBm models on the statistics of the resultant LnK fields and the dynamics of groundwater flow and solute transport revealed by this study shed light on the selection and evaluation of the fractional probability distribution models in capturing the K fields for low-K media.     

Variation in soil saturated hydraulic conductivity along the hillslope of collapsing granite gullies

Saturated hydraulic conductivity (K_s) affects the soil hydrological process and is influenced by many factors that exhibit strong spatial variations. To accurately measure K_s and its scale, spatial variability and relationship with collapsing gullies, we analysed four double-ring infiltrometer diameters in three soil layers during in situ experiments designed to measure K_s in two typical collapsing gullies (three slope sites) in Tongcheng County of China. The results showed that K_s increased with increasing inner ring diameter, but no significant difference existed between inner diameters of 30 and 40 cm. The K_s in red soil layers was higher than that in sandy soil layers, the transition layers had the lowest values. K_s also varied with slope position, gradually decreasing with distance from the gully head. The suggestion is that the spatial variation in K_s is affected not only by the intrinsic soil properties but also by the interaction with the collapsing gully.     

Changes in Entrapped Gas Content and Hydraulic Conductivity with Pressure

Water table fluctuations continuously introduce entrapped air bubbles into the otherwise saturated capillary fringe and groundwater zone, which reduces the effective (quasi‐saturated) hydraulic conductivity, K_quasi, thus impacting groundwater flow, aquifer recharge and solute and contaminant transport. These entrapped gases will be susceptible to compression or expansion with changes in water pressure, as would be expected with water table (and barometric pressure) fluctuations. Here we undertake laboratory experiments using sand‐packed columns to quantify the effect of water table changes of up to 250 cm on the entrapped gas content and the quasi‐saturated hydraulic conductivity, and discuss our ability to account for these mechanisms in ground water models. Initial entrapped air contents ranged between 0.080 and 0.158, with a corresponding K_quasi ranging between 2 and 6 times lower compared to the K_s value. The application of 250 cm of water pressure caused an 18% to 26% reduction in the entrapped air content, resulting in an increase in K_quasi by 1.16 to 1.57 times compared to its initial (0 cm water pressure) value. The change in entrapped air content measured at pressure step intervals of 50 cm, was essentially linear, and could be modeled according to the ideal gas law. Meanwhile, the changes in K_quasi with compression–expansion of the bubbles because of pressure changes could be adequately captured with several current hydraulic conductivity models.     

Artificial neural network models for estimating regional reference evapotranspiration based on climate factors

Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, an artificial neural network (ANN) model for reference evapotranspiration (ET₀) calculation was investigated. ANNs were trained and tested for arid (west), semi‐arid (middle) and sub‐humid (east) areas of the Inner Mongolia district of China. Three or four climate factors, i.e. air temperature (T), relative humidity (RH), wind speed (U) and duration of sunshine (N) from 135 meteorological stations distributed throughout the study area, were used as the inputs of the ANNs. A comparison was conducted between the estimates provided by the ANNs and by multilinear regression (MLR). The results showed that ANNs using the climatic data successfully estimated ET₀ and the ANNs simulated ET₀ better than the MLRs. The ANNs with four inputs were more accurate than those with three inputs. The errors of the ANNs with four inputs were lower (with RMSE of 0·130 mm d^?1, RE of 2·7% and R² of 0·986) in the semi‐arid area than in the other two areas, but the errors of the ANNs with three inputs were lower in the sub‐humid area (with RMSE of 0·21 mm d^?1, RE of 5·2% and R² of 0·961. For the different seasons, the results indicated that the highest errors occurred in September and the lowest in April for the ANNs with four inputs. Similarly, the errors were higher in September for the ANNs with three inputs. Copyright © 2008 John Wiley & Sons, Ltd.     

First approximations of soil moisture retention curves using the filter‐paper method

Relationships between gravimetric soil moisture content (w) and matric potential (ϕ), and between volumetric soil moisture content (θ_v) and pressure head (h) were approximated for the unsaturated zone on Long Island, New York. Soil samples were collected from two sites using a hand auger. The soil moisture content was determined using the filter‐paper (w_f) and gravimetric (w) methods, respectively. The w_f was then used in an empirical equation to estimate ϕ_m. Each set of ϕ_m and w was combined with a straight‐line empirical model to obtain a w(ϕ_m) relationship. Soil ϕ_m was converted to h, and w to the volumetric moisture content θ_v, in order to produce a θ_v(h) curve. Graphical and statistical comparison showed that the resulting θ_v(h) curves fell within one order of magnitude of similar curves generated by a more sophisticated non‐linear model developed previously. The simplicity and low cost of the filter‐paper approach described in this study recommends it for preliminary studies of hydraulic properties in the unsaturated zone. Copyright © 2000 John Wiley & Sons, Ltd.