Combining a Single Hydraulic Conductivity Measurement with Particle Size Distribution Data for Estimating the Full Range Partially Saturated Hydraulic Conductivity Curve

2014 ◽  
Vol 78 (5) ◽  
pp. 1594-1605 ◽  
Author(s):  
Mohammad Hossein Mohammadi ◽  
Mahnaz Khatar ◽  
Marnik Vanclooster
Keyword(s):  
Particle Size ◽  
Hydraulic Conductivity ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Full Range ◽  
Conductivity Measurement ◽  
Saturated Hydraulic Conductivity ◽  
Distribution Data ◽  
Partially Saturated ◽  
Conductivity Curve

scholarly journals Saturated Hydraulic Conductivity and Textural Heterogeneity of Soils

2017 ◽  
Author(s):  
Carlos García-Gutiérrez ◽  
Yakov Pachepsky ◽  
Miguel Ángel Martín
Keyword(s):  
Particle Size ◽  
Hydraulic Conductivity ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Information Entropy ◽  
Fine Sand ◽  
Saturated Hydraulic Conductivity ◽  
Coarse Particles ◽  
Heterogeneity Parameter

Abstract. Saturated hydraulic conductivity Ksat is an important soil parameter that highly depends on soil's particle size distribution (PSD). The nature of this dependency is explored in this work in two ways, (1) by using the Information Entropy as a heterogeneity parameter of the PSD and (2) using descriptions of PSD in forms of textural triplets, different than the usual description in terms of the triplet of sand, silt and clay contents. The power of this parameter, as a descriptor of Ksat and log(Ksat) , was tested on a database of > 19 K soils. We found coefficients of determination of up to 0.977 for log(Ksat) using a triplet that combines very coarse, coarse, medium and fine sand as coarse particles, very fine sand as intermediate particles, and silt and clay as fines. The power of the correlation is analysed for different textural classes and different triplets. Overall, the use of textural triplets different than traditional, combined with IE, may provide a useful tool for predicting Ksat values.

Predicting unsaturated hydraulic conductivity functions of three Indian soils from particle size distribution data

Soil Research ◽  
2003 ◽  
Vol 41 (8) ◽  
pp. 1457 ◽  
Author(s):  
S. K. Chaudhari ◽  
R. K. Batta
Keyword(s):  
Particle Size ◽  
Hydraulic Conductivity ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Distribution Data ◽  
Clay Loam ◽  
Modified Model ◽  
Physical Behaviour ◽  
Unsaturated Hydraulic Conductivity ◽  
Factor Α

The present study compares experimental unsaturated hydraulic conductivity functions, K(θ), of 9 soils of Maharashtra State of India, 3 each in the clay, clay loam, and loam textural classes, all predicted by a particle size distribution (PSD) based model. PSD data were transformed into pore-size distribution using Aryas' modified model. Experimental K(θ) curves were determined by the horizontal infiltration method. Fifteen soils, 5 each with clay, clay loam, and loam texture, were used to evaluate the parameters of flow model (i.e. qi = crix) empirically. These parameters deviated from Hagen-Poiseuilles' equation for an idealised porous medium. An effort was made to interpret the deviation in the logc and x values in relation to changes in hydro-physical behaviour of these soils. The model includes packing density and pore tortuosity as a scaling factor, α, of the Arya-Paris model. The model predicted hydraulic conductivity of these soils quite satisfactorily with R2 values for log-transformed experimental and predicted hydraulic conductivity being 0.85, 0.97, 0.94, and 0.92 for clay, clay loam, loam, and all textures together, respectively. The root mean square residues for the soils ranged from 0.067 to 0.724, with an average of 0.428. Prediction uncertainties in intra- and inter-textural classes were attributed to differences in hydro-physical behaviour of the soils.

Relationship between the Hydraulic Conductivity Function and the Particle-Size Distribution

2003 ◽  
Vol 67 (1) ◽  
pp. 373
Author(s):  
Lalit M. Arya ◽  
Feike J. Leij ◽  
Peter J. Shouse ◽  
Martinus Th. van Genuchten
Keyword(s):  
Particle Size ◽  
Hydraulic Conductivity ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Conductivity Function

Simplified estimation of unsaturated soil hydraulic conductivity using bulk electrical conductivity and particle size distribution

Soil Research ◽  
2013 ◽  
Vol 51 (1) ◽  
pp. 23 ◽  
Author(s):  
Mohammad Reza Neyshabouri ◽  
Mehdi Rahmati ◽  
Claude Doussan ◽  
Boshra Behroozinezhad
Keyword(s):  
Electrical Conductivity ◽  
Particle Size ◽  
Hydraulic Conductivity ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Unsaturated Soil ◽  
Soil Core ◽  
Soil Hydraulic Conductivity ◽  
Core Samples ◽  
Soil Hydraulic

Unsaturated soil hydraulic conductivity K is a fundamental transfer property of soil but its measurement is costly, difficult, and time-consuming due to its large variations with water content (θ) or matric potential (h). Recently, C. Doussan and S. Ruy proposed a method/model using measurements of the electrical conductivity of soil core samples to predict K(h). This method requires the measurement or the setting of a range of matric potentials h in the core samples—a possible lengthy process requiring specialised devices. To avoid h estimation, we propose to simplify that method by introducing the particle-size distribution (PSD) of the soil as a proxy for soil pore diameters and matric potentials, with the Arya and Paris (AP) model. Tests of this simplified model (SM) with laboratory data on a broad range of soils and using the AP model with available, previously defined parameters showed that the accuracy was lower for the SM than for the original model (DR) in predicting K (RMSE of logK = 1.10 for SM v. 0.30 for DR; K in m s–1). However, accuracy was increased for SM when considering coarse- and medium-textured soils only (RMSE of logK = 0.61 for SM v. 0.26 for DR). Further tests with 51 soils from the UNSODA database and our own measurements, with estimated electrical properties, confirmed good agreement of the SM for coarse–medium-textured soils (<35–40% clay). For these textures, the SM also performed well compared with the van Genuchten–Mualem model. Error analysis of SM results and fitting of the AP parameter showed that most of the error for fine-textured soils came from poorer adequacy of the AP model’s previously defined parameters for defining the water retention curve, whereas this was much less so for coarse-textured soils. The SM, using readily accessible soil data, could be a relatively straightforward way to estimate, in situ or in the laboratory, K(h) for coarse–medium-textured soils. This requires, however, a prior check of the predictive efficacy of the AP model for the specific soil investigated, in particular for fine-textured/structured soils and when using previously defined AP parameters.

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