Revisiting Vereecken Pedotransfer Functions: Introducing a Closed‐Form Hydraulic Model

Weynants, Mélanie; Vereecken, Harry; Javaux, Mathieu

doi:10.2136/vzj2008.0062

Cited by 195 publications

(226 citation statements)

References 36 publications

(50 reference statements)

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“…While it has been previously demonstrated that water flow in macropores cannot be accurately predicted with empirical models from basic soil properties (Weynants et al, 2009;Vereecken et al, 2010), there is only little published work related to gas diffusivity. Furthermore, existing pedotransfer functions/empirical models do not discern between matrixand biopore-dominated flow domains, which is of significance for understanding and accurate prediction of preferential flow as demonstrated in the results section.…”

Section: Introductionmentioning

confidence: 99%

Prediction of biopore- and matrix-dominated flow from X-ray CT-derived macropore network characteristics

Naveed

Møldrup

Schaap

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Prediction and modeling of localized flow processes in macropores is of crucial importance for sustaining both soil and water quality. However, currently there are no reliable means to predict preferential flow due to its inherently large spatial variability. The aim of this study was to investigate the predictive performance of previously developed empirical models for both water and air flow and to explore the potential applicability of X-ray computed tomography (CT)-derived macropore network characteristics. For this purpose, 65 cylindrical soil columns (6 cm diameter and 3.5 cm height) were extracted from the topsoil (5 cm to 8.5 cm depth) in a 15 m × 15 m grid from an agricultural field located in Silstrup, Denmark. All soil columns were scanned with an industrial X-ray CT scanner (129 µm resolution) and later employed for measurement of saturated hydraulic conductivity, air permeability at −30 and −100 cm matric potential, and gas diffusivity at −30 and −100 cm matric potential. Distribution maps for saturated hydraulic conductivity, air permeability, and gas diffusivity reflected no autocorrelation irrespective of soil texture and organic matter content. Existing empirical predictive models for saturated hydraulic conductivity and air permeability showed poor performance, as they were not able to realistically capture macropore flow. The tested empirical model for gas diffusivity predicted measurements at −100 cm matric potential reasonably well, but failed at −30 cm matric potential, particularly for soil columns with biopore-dominated flow. X-ray CT-derived macroporosity matched the measured airfilled porosity at −30 cm matric potential well. Many of the CT-derived macropore network characteristics were strongly interrelated. Most of the macropore network characteristics were also significantly correlated with saturated hydraulic conductivity, air permeability, and gas diffusivity. The predictive Ahuja et al. (1984) model for saturated hydraulic conductivity, air permeability, and gas diffusivity performed reasonably well when parameterized with novel, X-ray CTderived parameters such as effective percolating macroporosity for biopore-dominated flow and total macroporosity for matrix-dominated flow. The obtained results further indicate that it is crucially important to discern between matrixdominated and biopore-dominated flow for accurate prediction of macropore flow from X-ray CT-derived macropore network characteristics.

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Section: Introductionmentioning

confidence: 99%

Prediction of biopore- and matrix-dominated flow from X-ray CT-derived macropore network characteristics

Naveed

Møldrup

Schaap

et al. 2016

Hydrol. Earth Syst. Sci.

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“…Other methods are indirect, allowing a theoretical calculation of the function K(θ) from water retention data in the soil obtained in the laboratory Weynants et al, 2009). Both the direct and indirect methods are time-intensive, generate costs due to the need for pre-determinations of hydraulic properties, are error-prone, and the results are only valid on a local scale (Mermoud & Xu, 2006;Arya & Heitman, 2010).…”

Section: Introductionmentioning

confidence: 99%

Estimation of water percolation by different methods using TDR

Silva

Coelho

2014

Rev. Bras. Ciênc. Solo

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Detailed knowledge on water percolation into the soil in irrigated areas is fundamental for solving problems of drainage, pollution and the recharge of underground aquifers. The aim of this study was to evaluate the percolation estimated by time-domain-reflectometry (TDR) in a drainage lysimeter. We used Darcy's law with K(θ) functions determined by field and laboratory methods and by the change in water storage in the soil profile at 16 points of moisture measurement at different time intervals. A sandy clay soil was saturated and covered with plastic sheet to prevent evaporation and an internal drainage trial in a drainage lysimeter was installed. The relationship between the observed and estimated percolation values was evaluated by linear regression analysis. The results suggest that percolation in the field or laboratory can be estimated based on continuous monitoring with TDR, and at short time intervals, of the variations in soil water storage. The precision and accuracy of this approach are similar to those of the lysimeter and it has advantages over the other evaluated methods, of which the most relevant are the possibility of estimating percolation in short time intervals and exemption from the predetermination of soil hydraulic properties such as water retention and hydraulic conductivity. The estimates obtained by the Darcy-Buckingham equation for percolation levels using function K(θ) predicted by the method of Hillel et al. (1972) provided compatible water percolation estimates with those obtained in the lysimeter at time intervals greater than 1 h. The methods of Libardi et al. (1980), Sisson et al. (1980) and van Genuchten (1980) underestimated water percolation.

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“…K* (l T -1 ) is a matching point hydraulic conductivity for S e = 1. Although K* is usually deemed as the saturated hydraulic conductivity Ks, a fitted matching point conductivity for slightly unsaturated conditions, K*= K 0 <Ks, may be preferable for modelling unsaturated soils (Weynants et al, 2009).…”

Section: Methodsmentioning

confidence: 99%

Quantifying soil hydraulic properties and their uncertainties by modified GLUE method

Yan¹,

Liu²,

Zhang³

et al. 2017

International Agrophysics

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A b s t r a c t. Nonlinear least squares algorithm is commonly used to fit the evaporation experiment data and to obtain the 'optimal' soil hydraulic model parameters. But the major defects of nonlinear least squares algorithm include non-uniqueness of the solution to inverse problems and its inability to quantify uncertainties associated with the simulation model. In this study, it is clarified by applying retention curve and a modified generalised likelihood uncertainty estimation method to model calibration. Results show that nonlinear least squares gives good fits to soil water retention curve and unsaturated water conductivity based on data observed by Wind method. And meanwhile, the application of generalised likelihood uncertainty estimation clearly demonstrates that a much wider range of parameters can fit the observations well. Using the 'optimal' solution to predict soil water content and conductivity is very risky. Whereas, 95% confidence interval generated by generalised likelihood uncertainty estimation quantifies well the uncertainty of the observed data. With a decrease of water content, the maximum of nash and sutcliffe value generated by generalised likelihood uncertainty estimation performs better and better than the counterpart of nonlinear least squares. 95% confidence interval quantifies well the uncertainties and provides preliminary sensitivities of parameters.

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Revisiting Vereecken Pedotransfer Functions: Introducing a Closed‐Form Hydraulic Model

Cited by 195 publications

References 36 publications

Prediction of biopore- and matrix-dominated flow from X-ray CT-derived macropore network characteristics

Prediction of biopore- and matrix-dominated flow from X-ray CT-derived macropore network characteristics

Estimation of water percolation by different methods using TDR

Quantifying soil hydraulic properties and their uncertainties by modified GLUE method

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