Sensitivity of selected water retention functions to compaction and inherent soil properties

Soil Research ◽  
1998 ◽  
Vol 36 (2) ◽  
pp. 317 ◽  
Author(s):  
V. Rasiah ◽  
L. A. G. Aylmore
Keyword(s):  
Soil Properties ◽  
Water Retention ◽  
Linear Regression Analysis ◽  
Coefficient Of Determination ◽  
Strongly Correlated ◽  
Limited Information ◽  
Field Scale ◽  
Soil Water Retention ◽  
Landscape Unit ◽  
Retention Characteristics

It is known that field-scale variations in subsurface hydraulic characteristics are influenced, to a large extent, by soil properties. Limited information, however, exists on the sensitivity of hydraulic functions to field-scale variations in soil properties. The sensitivity of 4 soil water retention functions, θ(h), to variations in soil properties and changes in bulk density (ρ) across and within soils along a 500-m transect has been assessed in this study. The θ(h) functions compared are those of van Genuchten, Brooks and Corey, Campbell, and Gardner. Water retention characteristics for 7 soils, each packed to 2 relative ρ, were established for each function. The coefficient of determination, R 2 , for the best fit of water retention ranged from 0·79 to 0· 98 for the Gardner and Campbell functions, from 0· 92 to 0·99 for the Brooks and Corey function, and from 0·83 to 0·99 for the van Genuchten function. Simple linear regression analysis indicated the nonlinear slope parameters of the 4 functions were more strongly correlated with soil properties. However, only the van Genuchten slope parameters were sensitive to changes in ρ. No consistency existed between the sensitivity of the linear parameters of the 4 functions and soil properties, and none were sensitive to changes in ρ. Except for the a parameter in the van Genuchten function, all the parameters in this function can be predicted with satisfactory confidence from soil properties and ρ. The results indicate that, of the 4 functions assessed, the van Genuchten θ(h) function is the most sensitive to field-scale variations in soil properties along a transect in a landscape unit and to changes in ρ.

scholarly journals Pedotransfer functions for point and parametric estimations of soil water retention curve

2011 ◽  
Vol 52 (No, 7) ◽  
pp. 321-327 ◽  
Author(s):  
H. Merdun
Keyword(s):  
Soil Properties ◽  
Water Retention ◽  
Pedotransfer Functions ◽  
Water Retention Curve ◽  
Coefficient Of Determination ◽  
Retention Data ◽  
Parametric Methods ◽  
Soil Water Retention Curve ◽  
Soil Water Retention ◽  
Retention Curve

A water retention curve is required for the simulation studies of water and solute transport in unsaturated or vadose zone. Unlike the direct measurement of water retention data, pedotransfer functions (PTFs) have attracted the attention of researchers for determining water retention curves from basic soil properties. The objective of this study was to develop and validate point and parametric PTFs for the estimation of water retention curve from basic soil properties such as particle-size distribution, bulk density, and porosity using multiple-linear regression technique and comparing the performances of point and two parametric methods using some evaluation criteria. 140 soil samples were collected from three different databases and divided as 100 and 40 for the derivation and validation of the PTFs. All three methods predicted water contents at selected water potentials and combined water retention curves pretty well, but van Genuchten&rsquo;s model performed the best in prediction. However, the differences among the methods in point and water retention curve predictions were not statistically significant (p &gt; 0.05). Prediction accuracies were evaluated by the coefficient of determination (R<sup>2</sup>) and the root mean square error (RMSE) between the measured and predicted values. The R<sup>2</sup> and RMSE were 0.962 and 0.036, 0.994 and 0.067, and 0.946 and 0.082 for point and parametric (van Genuchten, and Brooks and Corey) methods, respectively, in predicting combined water retention curve. The three methods can be alternatively used in the estimation of water retention curves, but parametric methods are preferred for yielding continuous water retention functions used in flow and transport modeling.

Analysis of soil water retention characteristics (SWRC) models using R computer program

2021 ◽  
Author(s):  
Vitalis Kibiwott Too ◽  
Christian Thine Omuto ◽  
Elijah Kipngetich Biamah ◽  
John Paul Obiero
Keyword(s):  
Computer Program ◽  
Soil Water ◽  
Water Retention ◽  
Soil Water Retention ◽  
Retention Characteristics

scholarly journals Investigating soil water retention characteristics at high suctions using relative humidity control

2016 ◽  
Vol 9 ◽  
pp. 10007 ◽  
Author(s):  
Vasileios Mantikos ◽  
Steven Ackerley ◽  
Andrew Kirkham ◽  
Aikaterini Tsiampousi ◽  
David M.G. Taborda ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Soil Water ◽  
Water Retention ◽  
Soil Water Retention ◽  
Humidity Control ◽  
Retention Characteristics

Method for Quick Prediction of Hydraulic Conductivity and Soil-Water Retention of Unsaturated Soils

2018 ◽  
Vol 2672 (52) ◽  
pp. 108-117 ◽  
Author(s):  
Shaoyang Dong ◽  
Yuan Guo ◽  
Xiong (Bill) Yu
Keyword(s):  
Finite Element ◽  
Hydraulic Conductivity ◽  
Soil Properties ◽  
Soil Water ◽  
Unsaturated Soil ◽  
Unsaturated Soils ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Characteristic Curve ◽  
Soil Water Retention

Hydraulic conductivity and soil-water retention are two critical soil properties describing the fluid flow in unsaturated soils. Existing experimental procedures tend to be time consuming and labor intensive. This paper describes a heuristic approach that combines a limited number of experimental measurements with a computational model with random finite element to significantly accelerate the process. A microstructure-based model is established to describe unsaturated soils with distribution of phases based on their respective volumetric contents. The model is converted into a finite element model, in which the intrinsic hydraulic properties of each phase (soil particle, water, and air) are applied based on the microscopic structures. The bulk hydraulic properties are then determined based on discharge rate using Darcy’s law. The intrinsic permeability of each phase of soil is first calibrated from soil measured under dry and saturated conditions, which is then used to predict the hydraulic conductivities at different extents of saturation. The results match the experimental data closely. Mualem’s equation is applied to fit the pore size parameter based on the hydraulic conductivity. From these, the soil-water characteristic curve is predicted from van Genuchten’s equation. The simulation results are compared with the experimental results from documented studies, and excellent agreements were observed. Overall, this study provides a new modeling-based approach to predict the hydraulic conductivity function and soil-water characteristic curve of unsaturated soils based on measurement at complete dry or completely saturated conditions. An efficient way to measure these critical unsaturated soil properties will be of benefit in introducing unsaturated soil mechanics into engineering practice.

scholarly journals Pedotransfer functions of soil water properties to estimate the S-index

2018 ◽  
Vol 22 (7) ◽  
pp. 465-470
Author(s):  
João H. Caviglione
Keyword(s):  
Soil Water ◽  
Water Retention ◽  
Field Conditions ◽  
Pedotransfer Functions ◽  
Water Retention Curve ◽  
Coefficient Of Determination ◽  
Native Forest ◽  
Soil Water Retention Curve ◽  
Soil Water Retention ◽  
Soil Physical Quality

ABSTRACT One big challenge for soil science is to translate existing data into data that is needed. Pedotransfer functions have been proposed for this purpose and they can be point or parametric when estimating the water retention characteristics. Many indicators of soil physical quality have been proposed, including the S-Index proposed by Dexter. The objective of this study was to assess the use of pedotransfer functions for soil water retention to estimate the S-index under field conditions in the diversity of soils of the Paraná state. Soil samples were collected from 36 sites with textures ranging from sandy to heavy clay in the layers of 0-0.10 and 0.10-0.20 m and under two conditions (native forest and cultivated soil). Water content at six matric potentials, bulk density and contents of clay, sand and silt were determined. Soil-water retention curve was fitted by the van Genuchten-Mualem model and the S-index was calculated. S-index was estimated from water retention curves obtained by the pedotransfer function of Tomasella (point and parametric). Although the coefficient of determination varied from 0.759 to 0.895, modeling efficiency was negative and the regression coefficient between observed and predicted data was different from 1 in all comparisons. Under field conditions in the soil diversity of the Paraná state, restrictions were found in S-index estimation using the evaluated pedotransfer functions.

scholarly journals Capability of Diffuse Reflectance Spectroscopy to Predict Soil Water Retention and Related Soil Properties in an Irrigated Lowland District of Southern Italy

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1712
Author(s):  
Antonio Leone ◽  
Guido Leone ◽  
Natalia Leone ◽  
Ciro Galeone ◽  
Eleonora Grilli ◽  
...  
Keyword(s):  
Soil Properties ◽  
Soil Water ◽  
Water Retention ◽  
Southern Italy ◽  
Multivariate Calibration ◽  
Diffuse Reflectance Spectroscopy ◽  
Soil Analysis ◽  
Reflectance Spectroscopy ◽  
Reflectance Spectra ◽  
Soil Water Retention

In this study, we examined the potential of vis-NIR reflectance spectroscopy, coupled with partial least squares regression (PLSR) analysis, for the evaluation and prediction of soil water retention at field capacity (FC) and permanent wilting point (PWP) and related basic soil properties [organic carbon (OC), sand, silt, and clay contents] in an agricultural irrigated land of southern Italy. Soil properties were determined in the laboratory with reference to the Italian Official Methods for Soil Analysis. Vis-NIR reflectance spectra were measured in the laboratory, using a high-resolution spectroradiometer. All soil variables, with the exception of silt, evidently affected some specific spectral features. Multivariate calibrations were performed to predict the soil properties from reflectance spectra. PLSR was used to calibrate the spectral data using two-thirds of samples for calibration and one-third for validation. Spectroscopic data were pre-processed [multiplicative scatter correction (MSC), standard normal variance (SNV), wavelet detrending (WD), first and second derivative transformation, and filtering] prior to multivariate calibration. The results revealed very good models (2.0 < RPD < 2.5) for the prediction of FC, PWP and sand, and excellent (RPD > 2.5) models for the prediction of clay and OC, whereas a poor (RPD < 1.4) prediction model was obtained for silt.

Uncertainty analysis for large-scale prediction of the van Genuchten soil-water retention parameters with pedotransfer functions

Soil Research ◽  
2014 ◽  
Vol 52 (5) ◽  
pp. 431 ◽  
Author(s):  
K. Liao ◽  
S. Xu ◽  
J. Wu ◽  
Q. Zhu
Keyword(s):  
Soil Properties ◽  
Soil Water ◽  
Water Retention ◽  
Pedotransfer Functions ◽  
Soil Water Retention ◽  
Input Uncertainty ◽  
Spatial Estimation ◽  
Uncertainty Measurement ◽  
Error Bars ◽  
Retention Parameters

Hydrological, environmental and ecological modellers require van Genuchten soil-water retention parameters that are difficult to measure. Pedotransfer functions (PTFs) are thus routinely applied to predict hydraulic parameters (θs, ln(α) and n) from basic soil properties (e.g. bulk density, soil texture and organic matter content). This study investigated the spatial variations of van Genuchten parameters via geostatistical methods (e.g. kriging and co-kriging with remote-sensing data) and multiple-stepwise-regression-based PTFs with a limited number of samples (58) collected in Pingdu City, Shandong Province, China. The uncertainties in the spatial estimation of van Genuchten parameters were evaluated using bootstrap and Latin hypercube sampling methods. Results show that PTF-estimated parameters are less varied than observed parameters. The uncertainty in the parameter estimation is mainly due to the limited number of samples used for deriving PTFs (intrinsic uncertainty) and spatial interpolations of basic soil properties by (co)kriging (input uncertainty). When considering the intrinsic uncertainty, 36%, 29% and 47% of measurements are within the corresponding error bars (95% confidence intervals of the predictions) for the θs, ln(α) and n, respectively. When considering both intrinsic and input uncertainties, 86%, 66% and 88% of observations are within the corresponding error bars for the θs, ln(α) and n, respectively. Therefore, the input uncertainty is more important in the spatial estimation of van Genuchten parameters than the intrinsic uncertainty. Measurement of basic soil properties at high resolution and properly use of powerful spatial interpolation approach are both critical in the accurate spatial estimation of van Genuchten parameters.

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