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.

scholarly journals Assessment of pedotransfer functions for estimating soil water retention curves for the amazon region

2014 ◽  
Vol 38 (3) ◽  
pp. 730-743 ◽  
Author(s):  
João Carlos Medeiros ◽  
Miguel Cooper ◽  
Jaqueline Dalla Rosa ◽  
Michel Grimaldi ◽  
Yves Coquet
Keyword(s):  
Soil Water ◽  
Water Retention ◽  
Soil Layer ◽  
Direct Determination ◽  
Exchange Capacity ◽  
Pedotransfer Functions ◽  
Water Retention Curve ◽  
Spatial And Temporal Variability ◽  
Soil Water Retention Curve ◽  
Soil Water Retention

Knowledge of the soil water retention curve (SWRC) is essential for understanding and modeling hydraulic processes in the soil. However, direct determination of the SWRC is time consuming and costly. In addition, it requires a large number of samples, due to the high spatial and temporal variability of soil hydraulic properties. An alternative is the use of models, called pedotransfer functions (PTFs), which estimate the SWRC from easy-to-measure properties. The aim of this paper was to test the accuracy of 16 point or parametric PTFs reported in the literature on different soils from the south and southeast of the State of Pará, Brazil. The PTFs tested were proposed by Pidgeon (1972), Lal (1979), Aina & Periaswamy (1985), Arruda et al. (1987), Dijkerman (1988), Vereecken et al. (1989), Batjes (1996), van den Berg et al. (1997), Tomasella et al. (2000), Hodnett & Tomasella (2002), Oliveira et al. (2002), and Barros (2010). We used a database that includes soil texture (sand, silt, and clay), bulk density, soil organic carbon, soil pH, cation exchange capacity, and the SWRC. Most of the PTFs tested did not show good performance in estimating the SWRC. The parametric PTFs, however, performed better than the point PTFs in assessing the SWRC in the tested region. Among the parametric PTFs, those proposed by Tomasella et al. (2000) achieved the best accuracy in estimating the empirical parameters of the van Genuchten (1980) model, especially when tested in the top soil layer.

Multiscale Pedotransfer Functions for Soil Water Retention

2007 ◽  
Vol 6 (4) ◽  
pp. 868-878 ◽  
Author(s):  
Raghavendra B. Jana ◽  
Binayak P. Mohanty ◽  
Everett P. Springer
Keyword(s):  
Soil Water ◽  
Water Retention ◽  
Pedotransfer Functions ◽  
Soil Water Retention

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.

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