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Journal ArticleDOI

Improved Prediction of Unsaturated Hydraulic Conductivity with the Mualem-van Genuchten Model

TLDR
In this paper, the Mualem-van Genuchten model was used to predict unsaturated hydraulic conductivity from water retention parameters and neural network analyses confirmed that K 0 and L could indeed be predicted in this way.
Abstract
In many vadose zone hydrological studies, it is imperative that the soil's unsaturated hydraulic conductivity is known. Frequently, the Mualem-van Genuchten model (MVG) is used for this purpose because it allows prediction of unsaturated hydraulic conductivity from water retention parameters. For this and similar equations, it is often assumed that a measured saturated hydraulic conductivity (K s ) can be used as a matching point (K o ) while a factor S L e is used to account for pore connectivity and tortuosity (where S e is the relative saturation and L = 0.5). We used a data set of 235 soil samples with retention and unsaturated hydraulic conductivity data to test and improve predictions with the MVG equation. The standard practice of using K o = K, and L = 0.5 resulted in a root mean square error for log(K) (RMSE K ) of 1.31. Optimization of the matching point (K o ) and L to the hydraulic conductivity data yielded a RMSE K of 0.41. The fitted K 0 were, on average, about one order of magnitude smaller than measured K s . Furthermore, L was predominantly negative, casting doubt that the MVG can be interpreted in a physical way, Spearman rank correlations showed that both K 0 and L were related to van Genuchten water retention parameters and neural network analyses confirmed that K 0 and L could indeed be predicted in this way. The corresponding RMSE K was 0.84, which was half an order of magnitude better than the traditional MVG model. Bulk density and textural parameters were poor predictors while addition of K s improved the RMSE K only marginally. Bootstrap analysis showed that the uncertainty in predicted unsaturated hydraulic conductivity was about one order of magnitude near saturation and larger at lower water contents.

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Journal ArticleDOI

rosetta: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions

TL;DR: In this paper, the authors describe a computer program, rosetta, which implements five hierarchical pedotransfer functions (PTFs) for the estimation of water retention, and the saturated and unsaturated hydraulic conductivity.
Journal ArticleDOI

Development and Applications of the HYDRUS and STANMOD Software Packages and Related Codes

TL;DR: A review of the history of development, main processes involved, and selected applications of HYDRUS and related models and software packages developed collaboratively by several groups in the United States, the Czech Republic, Israel, Belgium, and the Netherlands can be found in this paper.
Journal ArticleDOI

Tortuosity in Porous Media: A Critical Review

TL;DR: The concept of tortuosity is used to characterize the structure of porous media, to estimate their electrical and hydraulic conductivity, and to study the travel time and length for tracer dispersion as mentioned in this paper.
Journal ArticleDOI

Using pedotransfer functions to estimate the van Genuchten-Mualem Soil Hydraulic Properties: a review.

TL;DR: In this article, the authors reviewed the use of the van Genuchten-Mualem (VGM) model to parameterize soil hydraulic properties and for developing pedotransfer functions (PTFs).
Journal ArticleDOI

Description of the unsaturated soil hydraulic database UNSODA version 2.0

TL;DR: The format and structure of the new database have been modified to provide additional and more convenient options for data searches, to provide compatibility with other programs for easy loading and downloading of data, and to allow users to customise the contents and look of graphical output.
References
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Book

Neural Networks: A Comprehensive Foundation

Simon Haykin
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal ArticleDOI

A simplex method for function minimization

TL;DR: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point.
Journal ArticleDOI

A closed-form equation for predicting the hydraulic conductivity of unsaturated soils

TL;DR: Van Genuchten et al. as mentioned in this paper proposed a closed-form analytical expression for predicting the hydraulic conductivity of unsaturated soils based on the Mualem theory, which can be used to predict the unsaturated hydraulic flow and mass transport in unsaturated zone.
Journal ArticleDOI

A new model for predicting the hydraulic conductivity of unsaturated porous media

Y. Mualem
TL;DR: In this article, a simple analytic model is proposed which predicts the unsaturated hydraulic conductivity curves by using the moisture content-capillary head curve and the measured value of the hydraulic conductivities at saturation.
Journal ArticleDOI

Estimating generalized soil-water characteristics from texture

TL;DR: In this article, the results from the recent statistical analyses were used to calculate water potentials for a wide range of soil textures, then these were fit by multivariate analyses to provide continuous potential estimates for all inclusive textures.
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