Estimate Soil Hydraulic Conductivity Research Articles

Estimating Soil Hydraulic Conductivity at the Field Scale With a State-Space Approach

A precise description of saturated (Ks) and near-saturated hydraulic conductivity (K−10) and their spatial variability is important for understanding water/solute transport in the vadose zone. However, it is laborious to measure K directly. Alternatively, K could be predicted from easily measurable soil properties using pedotransfer functions (PTFs). Because PTFs ignore the spatial relationships and covariance between soil variables, they often perform unsatisfactorily when field-scale estimations of K are needed. Therefore, the objective of this study was to improve the estimation of K at field scale through consideration of spatial dependences between soil variables. K was measured at 48 locations in a 71 × 71-m grid within a farmland under no-till. An autoregressive state-space approach was used to quantify the spatial relations between K and soil properties and to analyze the spatial variability of K in the field. In comparison, multiple linear regression (MLR) was used to derive PTFs for K estimation. Using various combinations of variables, state-space analysis outperformed PTFs in estimating spatial K distribution across the field. While state-space approach explained 69%, MLR method explained only 6% of the total variation in Ks. For K−10, the best state-space model included silt, clay, and macroporosity and performed almost perfectly (R >95%) in characterizing the spatial variability of K−10. In that case, the best MLR-type PTF explained only 60% of the variation. The results indicate that, by considering the spatial relations between soil variables, state-space approach is an effective tool for analyzing the spatial variability of K at field scale.

A convenient method to estimate soil hydraulic conductivity using electrical conductivity and soil compaction degree

Soil hydraulic conductivity (K) significantly affects subsurface flow and solute transport, but convenient measurement of K remains a challenge. Electrical conductivity (EC), which can be measured conveniently and accurately, can be quantitatively related to K because of the similarity between current movement and groundwater flow, motivating us to estimate K using EC. A soil column apparatus was built to explore the relation between EC and K. Fifty-four experiments were conducted under various degrees of soil compaction and grain diameters, including six different particle sizes, three compaction levels, and six solution concentrations. The overall (negative) correlation between K and EC can be fitted reasonably well by an exponential function with a large determination coefficient (>0.81) for each solute concentration, while a power-law function better fits the correlation when K is larger than 10 m/d. These experiments led to several empirical models (for specific solution concentrations) and one comprehensive model (with variable parameters) to estimate K given EC. In the field, if the EC of pore water can be measured, the empirical models under the specific solution concentrations can be applied to greatly improve the K estimation. Comparison showed that the proposed models, which provides a quick estimate of K using easily available EC, performed better than most of the classical grain size-based formulas. Although there is uncertainty in the K estimation likely due to the limited control factors considered in this study, the new method proposed above can provide a convenient estimate for K for the field site with strong spatial heterogeneity.

Effect of Stoniness on the Hydraulic Properties of a Soil from an Evaporation Experiment Using the Wind and Inverse Estimation Methods

Stony soils are distributed all over the world. The study of their characteristics has gained importance lately due to their increasing use as agricultural soils. The effect that rock fragments exert on the soil hydraulic properties is difficult to measure in situ, and is usually derived from the fine earth properties. However, the corrections used so far do not seem accurate for all types of stony soils. Our objective was to assess the adequacy of estimating the hydraulic properties of a stony soil from the fine earth ones by correcting the latter by the volume occupied by rock fragments. To do that, we first assessed the validity of different approaches for estimating the hydraulic properties of a stone-free and a stony (40% rock fragments) cylinder prepared with samples from the same silt loam soil. The functions relating to the soil hydraulic properties (θ-h, K-h-θ) were estimated by the Wind method and by inverse estimation, using data from an evaporation experiment where the soil water content and pressure head were measured at different soil depths over time. Results from the evaporation experiment were compared to those obtained by applying the equation that corrects fine earth properties by the rock fragments volume. Wind and the Inverse Estimation methods were successfully applied to estimate soil water content and hydraulic conductivity from the stony soil experiment, except for some uncertainties caused by the limited range of suction in which the experiment was conducted. The application of an equation for adjusting the soil water content at different pressure heads (allowing for defining the soil water retention curve, SWRC), and the unsaturated hydraulic conductivity (K) directly from the stone content was not satisfactory. K values obtained from the measured data were higher than those inferred by the correcting equation in the wet range, but decreased much faster with a decreasing pressure head. The use of this equation did therefore not take into account the effect that the creation of lacunar pores by the presence of rock fragments likely exerts on water flow processes. The use of such correction needs therefore to be revised and new approaches are needed for estimating the hydraulic conductivity in stony soils. In relation to SWRC, a new equation to calculate the water content of a stony soil accounting for the influence of possible lacunar pores is proposed.

Estimating soil hydraulic conductivity using different data-driven models of ANN, GMDH and GMDH-HS

The saturated hydraulic conductivity ( $$K_{\text{s}}$$ ) is one of the important soil hydraulic properties which plays a significant role in developing flow transport models and irrigation and drainage practices. In this research, artificial neural networks approaches, group method of data handling (GMDH) model and a hybrid intelligent model based on combination of GMDH and harmony search (HS) model (GMDH-HS) were developed to estimate $$K_{\text{s}}$$ based on 151 field samples collected from the northeast of Iran. Eleven topsoil properties were used as input parameters to estimate $$K_{\text{s}}$$ . The five quantitative standard statistical performance evaluation measures, i.e., coefficient of efficiency, root-mean-square error, mean square relative error, mean absolute percentage error and relative bias, were employed to evaluate the performance of various developed models. Statistical results indicated that the best performance can be obtained by GMDH-HS in terms of different evaluated criteria during the training and testing datasets for $$K_{\text{s}}$$ estimation.

An improved Mualem–Van Genuchten method and its verification using data on Beit Netofa clay

An improved Mualem–Van Genuchten method for estimating soil hydraulic conductivity in the vadose zone from data on filtration coefficient and water-retention capacity of soil is proposed. The approach offered by Kosugi for a functional description of hydraulic conductivity of soil is applied. To calculate the values of hydraulic conductivity by Mualem’s formula, the function of differential water capacity of soil with interpreted parameters has been used instead of the function of integral water capacity of soil, which describes the water-retention capacity of soil. Approximations to functions of soil water-retention capacity and hydraulic conductivity are offered here. On the basis of some concepts on the specificity of the curve that describes soil water-retention capacity, a technique for identification of these parameters is developed. The experimental data from two parts of capillary pressure range, on which the water-retention capacity of soil is measured, are used in the technique. The first part corresponds to the zone of mainly film moisture, where the sorption component of the capillary-sorption forces retaining the water in the soil predominates. The second part includes (a) the zone of the mainly capillary-suspended moisture, where the capillary component of the capillary-sorption forces predominates, and (b) the zone of the capillary-backed moisture. The improved method for estimating relative values of hydraulic conductivity of soil has been verified with the use of measured data for the Beit Netofa Сlay soil. The advantages of this method include (a) the ability to identify parameters of the soil hydrophysical functions using relatively available soil indices and (b) the higher accuracy of calculating the soil hydraulic conductivity in comparison with the original version of the method.

Using pedotransfer functions to estimate soil hydraulic conductivity in the Loess Plateau of China

Soil hydraulic conductivity (Ks) is a crucial soil physical property that not only influences soil hydrological processes, but also the planning for vegetation recovery, irrigation practice and drainage design. However, Ks data are often lacking at large-scale soil database due to difficulties in direct measurement that is often labour intensive, time consuming and cost inefficient. The objective of this study was to compare the performance of different emerging methods [Multiple linear regression (MLR) and artificial neural network (ANN)] of Ks prediction. The pedotransfer function (PTF) is one such method that is based on selected factors closely correlated with Ks at regional scale. We collected disturbed and undisturbed soil samples in the 0–40cm soil layer at 243 sites across the entire typical Loess Plateau of China (430,000km2) and then measured Ks and the potentially related factors. The results showed that Ks was normally distributed with moderate a spatial variation (CV=67%). Correlation analysis indicated that bulk density (BD), saturated soil water content (SSWC), clay content (Clay), silt content (Silt) and latitude were closely correlated (p<0.05) with Ks. Although the accuracies of MLR and ANN were equal in terms of estimating Ks, the stability of PTF developed via ANN was not as good as that of MLR. Thus PTF developed via MLR, which included BD, Silt and Clay, was considered as the best model for estimating Ks. There is a need to closely monitor the stability and repeatability of PTF during comparison and determination of PTF.

A method for estimating soil water diffusivity from moisture profiles and its application across an experimental catchment

Summary Despite the well-accepted value of soil hydraulic properties for describing and modeling matter and energy fluxes in the unsaturated zone, their accurate measurement across scales is still a daunting task. The increasing availability of continuous soil water content measurements at discrete points in space, as provided by sensor networks, offers still unexplored possibilities for evaluating soil physical properties across landscapes. In this study, we propose a new method, based on the Bruce and Klute equation, to estimate effective soil water diffusivity from soil water profile data observed during continuous desiccation periods. An analytical expression is proposed for the diffusion-soil water relationship, assuming an exponential relationship between soil water content and the Boltzmann variable. The method has been evaluated using soil water profile data observed at inter-row and under canopy locations across a rainfed olive orchard in SW Spain. The spatial variability of the effective soil water diffusivity across the orchard was estimated. Different soil conditions under the tree canopies as compared to inter-row areas resulted in significantly different effective diffusivity relationships, reflecting the effect of trees on soil physical properties and water dynamics across olive orchards. The proposed method offers a suitable alternative to traditional laboratory methods and can be easily extended to estimate soil hydraulic conductivity and water retention curves.

Influence of soil, land use and climatic factors on the hydraulic conductivity of soil

Abstract. Due to inadequate data support, existing algorithms used to estimate soil hydraulic conductivity, K, in (eco)hydrological models ignore the effects of key site factors such as land use and climate and underplay the significant effects of soil structure on water flow at and near saturation. These limitations may introduce serious bias and error into predictions of terrestrial water balances and soil moisture status, and thus plant growth and rates of biogeochemical processes. To resolve these issues, we collated a new global database of hydraulic conductivity measured by tension infiltrometer under field conditions. The results of our analyses on this data set contrast markedly with those of existing algorithms used to estimate K. For example, saturated hydraulic conductivity, Ks, in the topsoil (&lt; 0.3 m depth) was found to be only weakly related to texture. Instead, the data suggests that Ks depends more strongly on bulk density, organic carbon content and land use. In this respect, organic carbon was negatively correlated with Ks, presumably due to water repellency, while Ks at arable sites was, on average, ca. 2–3 times smaller than under natural vegetation, forests and perennial agriculture. The data also clearly demonstrates that clay soils have smaller K in the soil matrix and thus a larger contribution of soil macropores to K at and near saturation.

Ensemble pedotransfer functions to derive hydraulic properties for New Zealand soils

Modelling water and solute transport through soil requires the characterisation of the soil hydraulic functions; however, determining these functions based on measurements is time-consuming and costly. Pedotransfer functions (PTFs), which make use of easily measurable soil properties to predict the hydraulic functions, have been proposed as an alternative to measurements. The better known and more widely used PTFs were developed in the USA or Europe, where large datasets exist. No specific PTFs have been published for New Zealand soils. To address this gap, we evaluated a range of published PTFs against an available dataset comprising a range of different soils from New Zealand and selected the best PTFs to construct an ensemble PTF (ePTF). Assessment (and adjustment when required) of published PTFs was done by comparing measurements and estimates of soil water content and the hydraulic conductivity at selected matric suction values. For each point, the best two or three PTFs were chosen to compose the ePTF, with correcting constants if needed. The outputs of the ePTF are the hydraulic properties at selected matric suctions, akin to obtaining measurements, thus allowing the fit of different equations as well as combining any available measurements. Testing of the ePTF showed promising performance, with reasonably accurate estimates of the water retention of an independent dataset. Root mean square error values averaged 0.06 m3 m–3 for various New Zealand soils, which is within the accuracy level of published PTF studies. The largest errors were found for soils with high clay content, for which the ePTF should be used with care. The performance of the ePTF for estimating soil hydraulic conductivity was not as reliable as for water content, exhibiting large scatter. Predictions of saturated hydraulic conductivity were of the same magnitude as the measurements, whereas the unsaturated values were generally under-predicted. The conductivity data available for this study were limited and highly variable. The estimates for hydraulic conductivity should therefore be used with much care, and future research should address measurements and analysis to improve the predictions. The ePTF was also used to parameterise the SWIM soil module for use in Agricultural Production Systems Simulator (APSIM) simulations. Comparisons of drainage predicted by APSIM against results from lysimeter experiments suggest that the use of the derived ePTF is suited for the estimation of soil parameters for use in modelling. The ePTF is not envisaged as a substitute for measurements but is a useful tool to complement datasets with limited amounts of measured data.

Inverse Solution of Soil-Water Transport Model Parameters Using Response Surface Methodology

<emph italic=no/>Soil hydraulic properties influence crop yield, soil fertility, and soil water status. While the laboratory or direct methods of measuring hydraulic properties are accurate, they are expensive, time consuming, and may not represent in-situ properties. On the other hand, model-based inverse solution procedures or indirect methods of estimating soil hydraulic conductivity (K) relate primary model variables such as volumetric water content (<emph>θ</emph>) and soil water suction (<emph>ψ</emph>) to K and the soil-water characteristic curve. In this study, an inverse solution technique was developed to determine saturated soil hydraulic conductivity (K<emph subscript=yes>s</emph>, cm h<emph superscript=yes>-1</emph>) and plant conductance to water uptake (C, m<emph superscript=yes>-1</emph> min<emph superscript=yes>-1</emph>) under field conditions using a 1-D water transport model. The soil water content data (<emph>θ</emph>) were measured at 12 locations and at several depths at each location within the root zone in two different alfalfa checks using access tubes (each 1.5 m deep) and a neutron moisture probe. The inverse solution process utilized the measured soil moisture conditions in the alfalfa checks between two successive irrigations. In this study, the soil water retention curve was assumed to be of the form described by van Genuchten. The water contents at upper (0.225 m) and lower (1.2 m) depths provided the boundary conditions (BCs) needed for the model. The water content readings immediately following the irrigation event (i.e., t = 0) were used as the initial condition (IC). The model was solved to develop third-order orthogonal response surfaces over a range of model parameters (K<emph subscript=yes>s</emph> and C). Moreover, a multiple linear regression technique was used to empirically relate experimental <emph>θ</emph> to the depth and time of measurements. The response surface solutions were then optimized against the empirical surface to seek the optimum values of K<emph subscript=yes>s</emph> and C. The parameters estimated by the inverse solution technique were found to be unique and robust, and they predicted experimentally observed soil water data very well (R<emph superscript=yes>2</emph> > 0.95 and slope = 1:1 for most of the cases). These parameters, known from the inverse solution technique, were used to estimate the amount of deep percolation beyond the root zone depth for all cases.

Comparing the inverse parameter estimation approach with pedo-transfer function method for estimating soil hydraulic conductivity

The soil hydraulic conductivity was evaluated by the inverse parameter estimation method using the cumulative infiltration data measured by the tension infiltrometer. The inverse parameter estimation approach combines the Levenberg-Marquardt nonlinear optimization method with the variably saturated flow equation. In addition to the inverse parameter estimation method, soil hydraulic conductivity was also determined by various pedotransfer functions (PTF) which can be used to estimate soil hydraulic conductivity indirectly. The soil texture contents and organic carbon content were measured at experimental sites and used as input variables for PTF. A measure of root mean square error (RMSE) and mean absolute error (MAE) was utilized to compare soil hydraulic conductivity values between numerical inverse solution and PTF. The comparison of various PTFs indicated that PTF of Wosten et al. (1999) combined with the PTF of Cosby et al. (1984) was the best predictor for saturated hydraulic conductivity compared to the inverse solution. For unsaturated hydraulic conductivity, the PTF of Schaap (1999, SSC) showed the best prediction. The accuracy of various PTFs is highly variable depending on the saturated water content information. The results and method presented in the paper can be used as a hypothesis for further investigation and formulation of PTF applicable to soil condition in Korea.

Saturated Hydraulic Conductivity of Semiarid Soils: Combined Effects of Salinity, Sodicity, and Rate of Wetting

Combined effects of soil conditions (wetting rate), soil sodicity, and salinity on soil saturated hydraulic conductivity (HC) have not been studied systematically and were the objective of our study. We examined the effects of (i) exchangeable sodium percentage (ESP, 1–20) and fast wetting (50 mm h−1) and leaching with distilled water on the HC of 60 Israeli soils (7–70% clay); and (ii) wetting rate (2 or 50 mm h−1), ESP and water salinity (distilled water or saline water, 2 dS m−1) on the HC of 16 selected samples. Results of the first experiment showed that (i) steady state HC of medium‐ and fine‐textured soils was lower than 2 cm h−1 already for nonsodic soils, and (ii) the adverse impact of sodicity on the HC strongly depended on soil texture. The second experiment revealed that in the loamy sand rate of wetting had no effect on the HC beyond that of sodicity and salinity. In the loam, sandy clay and clay soils a significant triple interaction among water quality, wetting rate and ESP in their effect on HC existed. In the absence of electrolytes, the impact of fast wetting (slaking) and swelling on the HC was most notable, mainly at the intermediate sodicity levels (ESP = 5–10). Use of saline water significantly reduced the impact of fast wetting and swelling on the HC. Our results suggested that combined effects of salinity, wetting rate, and sodicity on the HC were complex and should thus be considered simultaneously when estimating soil HC.

A parameterised equation to estimate soil hydraulic conductivity in the field

The description of soil water dynamics using the Darcy–Buckingham approach involves the determination and use of soil hydraulic conductivity K v. soil water content θ functions. Many of the methods developed for the measurement of K are based on simplifying assumptions, such as the unit gradient and the choice of fixed models for the K(θ) relation. The need of quick, simple, and inexpensive methods to measure K(θ) in the field using a large number of replicates has also led soil physicists to develop simple methods. This paper presents a procedure that makes use of parameters of equations used to explain the internal water drainage process, and that naturally leads to the exponential character of the K(θ) relation. Results show that the parameterised equation represents a more rigorous estimation of K(θ), compared with the methods that assume unit gradient.

Libardi's method refinement for soil hydraulic conductivity measurement

The method of Libardi to estimate soil hydraulic conductivity in the field, during the redistribution of soil water, is discussed and improved. It is shown that if the saturated soil water content is measured at the soil surface, values at any other depth can be calculated from the database used to compute hydraulic conductivity. Since the saturated soil water content is difficult to measure and critical to the establishment of the hydraulic conductivity functions, this is an important refinement of the method. It is also shown that the unit hydraulic gradient assumption, which is part of the methodology, does not introduce significant errors in the estimation of soil hydraulic conductivity.

Estimating Soil Hydraulic Conductivity and Macroscopic Capillary Length from the Disk Infiltrometer

AbstractThe disk infiltrometer is increasingly used to measure in situ soil hydraulic properties in the field. Several procedures have been proposed for determining soil hydraulic conductivity from disk infiltrometer data. However, the procedures require steady‐state infiltration rates at several tensions, which limits their applications in the field. In this study, two methods were developed to estimate soil hydraulic conductivity and macroscopic capillary length using soil water fluxes at any infiltration time under the disk infiltrometer. The proposed procedures do not require steady‐state infiltration rates but infiltration data at any time. Therefore, the time required for field infiltrometer experiments can be dramatically reduced. Meanwhile the new procedures provide high estimation accuracy of soil hydraulic conductivity and macroscopic capillary length. Extensive numerically simulated data were used to examine the methods for various soils and different radii and tensions of the disk infiltrometer. The proposed methods are especially useful for estimating soil hydraulic conductivity and macroscopic capillary length of fine‐textured soils, using infiltration data collected during a short experiment period with a relatively large infiltrometer.

Use of an inverse method and geostatistics to estimate soil hydraulic conductivity for spatial variability analysis

A field method for determining the soil hydraulic properties using a parameter estimation technique is presented. Input data for the inverse problem are soil-water potentials and soil-water contents measured at different soil depths and different times during a field transient drainage experiment. For the water retention function the parametric relation suggested by Van Genuchten was adopted. For the hydraulic conductivity function the relation proposed by Van Genuchten and the exponential relation were adopted. With the proposed method soil hydraulic properties along a transect of a volcanic Vesuvian soil were determined using as boundary condition the unit gradient of total potential at the bottom of the soil profile. Geostatistics were used to describe the spatial variability of hydraulic conductivity characteristics of the soil here considered. Finally, results obtained using this method were compared with those of the simplified method suggested by Sisson and Van Genuchten based on a unit gradient water flow model.

USING SOIL PHYSICAL PROPERTIES TO ESTIMATE HYDRAULIC CONDUCTIVITY

We measured the hydraulic conductivity of a coarse-textured C horizon at 15 sites. The hydraulic conductivity over the soil water potential range of 0 to - 10 kPa was measured in situ with the crust test method, and the data were fit to a simple empirical equation. The resulting regression coefficients were found to be significantly correlated at the 1% confidence level with the percentage of sand, percentage of silt, bulk density, and porosity of the soil. An equation based only on the sand content of the soil was developed that described 83% of the hydraulic conductivity variation. The equation also successfully described the hydraulic conductivity of a soil with similar physical properties, but was inadequate when applied to a fine-textured soil. The technique shows potential of serving as an easy, reliable, and accurate means for estimating soil hydraulic conductivity.