Equation for unimodal and bimodal soil–water characteristic curves

Wijaya, Martin; Leong, Eng Choon

doi:10.1016/j.sandf.2016.02.011

Cited by 60 publications

(77 citation statements)

References 21 publications

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“…For example, Li et al () proposed a bimodal water retention curve and bimodal permeability function to describe the dual‐porosity soil structure, and they further developed regression models to estimate the bimodal retention curve parameters based on soil grain size distribution and void ratio. Recent work for bimodal functions includes Wijaya and Leong () and Zhai et al ().…”

Section: Introductionmentioning

confidence: 99%

A High‐Resolution Global Map of Soil Hydraulic Properties Produced by a Hierarchical Parameterization of a Physically Based Water Retention Model

Zhang

Schaap

Zha

2018

Water Resources Research

127

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A correct quantification of mass and energy exchange processes among Earth's land surface, groundwater, and atmosphere requires an accurate parameterization of soil hydraulic properties. Pedotransfer functions (PTFs) are useful in this regard because they estimate these otherwise difficult to obtain characteristics using texture and other ubiquitous soil data. Most PTFs estimate parameters of empirical hydraulic functions with modest accuracy. In a continued pursuit of improving global-scale PTF estimates, we evaluated whether improvements can be obtained when estimating parameters of hydraulic functions that make physically based assumptions. To this end, we developed a PTF that estimates the parameters of the Kosugi retention and hydraulic conductivity functions (Kosugi, 1994, https://doi.org/10.1029/93WR02931, 1996, https://doi.org/10.1029/96WR01776), which explicitly assume a lognormal pore size distribution and apply the Young-Laplace equation to derive a corresponding pressure head distribution. Using a previously developed combination of machine learning and bootstrapping, the developed five hierarchical PTFs allow for estimates under practical data-poor to data-rich conditions. Using an independent global data set containing nearly 50,000 samples (118,000 retention points), we demonstrated that the new Kosugi-based PTFs outperformed two van Genuchten-based PTFs calibrated on the same data. The new PTFs were applied to a 1 × 1 km 2 global map of texture and bulk density, thus producing maps of the parameters, field capacity, wilting point, plant available water, and associated uncertainties. Soil hydraulic parameters exhibit a much larger variability in the Northern Hemisphere than in the Southern Hemisphere, which is likely due to the geographical distribution of climate zones that affect weathering and sedimentation processes.

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Section: Introductionmentioning

confidence: 99%

A High‐Resolution Global Map of Soil Hydraulic Properties Produced by a Hierarchical Parameterization of a Physically Based Water Retention Model

Zhang

Schaap

Zha

2018

Water Resources Research

127

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“…Literature on soils with bimodal SWCC are limited. Generally, there are three approaches to obtain bimodal SWCC equation (Wijaya and Leong, 2016): piecewise, fraction volume and unique parameter. In the piecewise approach, the bimodal SWCC is separated into two unimodal segments connected at a point.…”

Section: Bimodal Swccmentioning

confidence: 99%

“…In the piecewise approach, the bimodal SWCC is separated into two unimodal segments connected at a point. The location of the point has a significant effect on the parameters of the SWCC equation and the parameters are highly non-unique (Wijaya and Leong, 2016). This approach may not give a smooth transition between the two unimodal segments.…”

Section: Bimodal Swccmentioning

confidence: 99%

“…As the number of parameters in the bimodal SWCC equation is more than that in the unimodal SWCC equation, determining of the parameters is not easy The unique parameter approach incorporates some or all graphically determined parameters to reduce the number of the parameters needed in the bimodal SWCC equation. The third approach is more attractive as less parameters need to be determined (Wijaya and Leong, 2016). Wijaya and Leong (2016) propose the following equation which can be used to describe unimodal and bimodal SWCCs:…”

Section: Bimodal Swccmentioning

confidence: 99%

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Soil-water characteristic curves - Determination, estimation and application

Leong

2019

JGS Special Publication

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The soil-water characteristic curve (SWCC) is a relationship between water content in a soil and suction. The SWCC was first plotted by Edgar Buckingham, a soil physicist, in 1907 for six soils ranging in texture from sand to clay. It was adopted for use in unsaturated soil mechanics by the geotechnical engineering community. The SWCC is now almost treated as the index property of unsaturated soils. It has been used as a proxy for permeability and shear strength of unsaturated soil. Most soils have a sigmoidal SWCC, otherwise known as a unimodal SWCC as opposed to a bimodal SWCC which has been found for some soils. Although determining the SWCC is easier than determining permeability or shear strength for unsaturated soil, the test is still time-consuming and it is not easy to determine the entire SWCC. Incomplete or insufficient SWCC data may lead to an incorrect SWCC and hence inaccurate determination of permeability and shear strength. Progress has been made to expedite the experimental determination of SWCC as well as estimating the SWCC from basic soil properties using pedotransfer functions. In addition, SWCC has been represented using volumetric water content, gravimetric water content or degree of saturation. Different representations may have dire consequences on its application. Determining the SWCC using volumetric water content or degree of saturation presents challenges in estimating the instantaneous volume during the experiment. In this paper, the current state-of-the-art in determining, representing and estimating SWCC and its applications are described and critically examined.

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“…The net vertical stress and temperature will influence the air-entry value of the SWCC. The SWCC is also affected by the number of samples, the predicting model and the model parameters estimation method [10,11], and hence the SWCC model parameters are subject to high uncertainty [12,13]. A better estimation of SWCC is expected for a given soil class through statistical analysis of SWCC distribution, where the model parameters can be regarded as random variables [14,15].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty of the Soil–Water Characteristic Curve and Its Effects on Slope Seepage and Stability Analysis under Conditions of Rainfall Using the Markov Chain Monte Carlo Method

Liu

Luo

Huang

et al. 2017

Water

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Abstract:It is important to determine the soil-water characteristic curve (SWCC) for analyzing slope seepage and stability under the conditions of rainfall. However, SWCCs exhibit high uncertainty because of complex influencing factors, which has not been previously considered in slope seepage and stability analysis under conditions of rainfall. This study aimed to evaluate the uncertainty of the SWCC and its effects on the seepage and stability analysis of an unsaturated soil slope under conditions of rainfall. The SWCC model parameters were treated as random variables. An uncertainty evaluation of the parameters was conducted based on the Bayesian approach and the Markov chain Monte Carlo (MCMC) method. Observed data from granite residual soil were used to test the uncertainty of the SWCC. Then, different confidence intervals for the model parameters of the SWCC were constructed. The slope seepage and stability analysis under conditions of rainfall with the SWCC of different confidence intervals was investigated using finite element software (SEEP/W and SLOPE/W). The results demonstrated that SWCC uncertainty had significant effects on slope seepage and stability. In general, the larger the percentile value, the greater the reduction of negative pore-water pressure in the soil layer and the lower the safety factor of the slope. Uncertainties in the model parameters of the SWCC can lead to obvious errors in predicted pore-water pressure profiles and the estimated safety factor of the slope under conditions of rainfall.

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Equation for unimodal and bimodal soil–water characteristic curves

Cited by 60 publications

References 21 publications

A High‐Resolution Global Map of Soil Hydraulic Properties Produced by a Hierarchical Parameterization of a Physically Based Water Retention Model

A High‐Resolution Global Map of Soil Hydraulic Properties Produced by a Hierarchical Parameterization of a Physically Based Water Retention Model

Soil-water characteristic curves - Determination, estimation and application

Uncertainty of the Soil–Water Characteristic Curve and Its Effects on Slope Seepage and Stability Analysis under Conditions of Rainfall Using the Markov Chain Monte Carlo Method

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