A Stochastic Model for Determining Soil Hydraulic Properties from Particle Size Distribution

2003 ◽  
Author(s):  
T. P. Chan ◽  
R. S. Govindaraju
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Stochastic Model ◽  
Size Distribution ◽  
Hydraulic Properties ◽  
Soil Hydraulic Properties ◽  
Soil Hydraulic

Predicting water retention curves of fine texture soils from particle size distribution

2020 ◽  
Author(s):  
Joseph Pollacco ◽  
Jesús Fernández-Gálvez ◽  
Sam Carrick
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Pore Space ◽  
Water Retention Curve ◽  
Soil Hydraulic Properties ◽  
Retention Curve ◽  
Soil Particles

<p>Indirect methods for estimating soil hydraulic properties from particle size distribution have been developed due to the difficulty in accurately determining soil hydraulic properties, and the fact that particle size distribution is one piece of basic soil physical information normally available. The similarity of the functions describing the cumulative distribution of particle size and pore size in the soil has been the basis for relating particle size distribution and the water retention function in the soil. Empirical and semi-physical models have been proposed, but these are based on strong assumptions that are not always valid. For example, soil particles are normally assumed to be spherical, with constant density regardless of their size; and the soil pore space has been described by an assembly of capillary tubes, or the pore space in the soil matrix is assumed to be arranged in a similar way regardless of particle size. However, in a natural soil the geometry of the pores may vary with the size of the particles, leading to a variable relation between particle radius and pore radius.</p><p> </p><p>The current work is based on the hypothesis that the geometry of the pore size and the void ratio depends on the size of the soil particles, and that a physically based model can be generalised to predict the water retention curve from particle size distribution. The rearrangement of the soil particles is considered by introducing a mixing function that modulates the cumulative particle size distribution, while the total porosity is constrained by the saturated water content.</p><p> </p><p>The model performance is evaluated by comparing the soil water retention curve derived from laboratory measurements with a mean Nash–Sutcliffe model efficiency a value of 0.92 and a standard deviation of 0.08. The model is valid for all soil types, not just those with a marginal clay fraction.</p>

scholarly journals Fitting soil particle-size distribution (PSD) models by PSD curve fitting software

2019 ◽  
Vol 52 (2) ◽  
pp. 211
Author(s):  
Mostafa Rastgou ◽  
Hossein Bayat ◽  
Muharram Mansoorizadeh
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Mathematical Models ◽  
Size Distribution ◽  
Curve Fitting ◽  
Hydraulic Properties ◽  
Soil Samples ◽  
Statistical Properties ◽  
Water Retention Curve ◽  
Soil Hydraulic

This paper describes a particle-size distribution (PSD) curve fitting software for analyzing the soil PSD and soil physical properties. A better characterization of soil texture can be obtained by describing the soil PSD using mathematical models. The mathematical equations of soil PSD are mainly used as a basis to estimate the soil hydraulic properties. Until now, many attempts are made to represent PSD curves using mathematical models, but selecting the best PSD model requires fitting all models to the PSD data, which would be difficult and time-consuming. So far, no specific program has been developed to fit the PSD models to the experimental data. A practical user-friendly software called "PSD Curve Fitting Software" was developed and introduced to program a simultaneous fitting of all models on soil PSD data of all samples. Some of the capabilities of this software are calculating evaluation statistics for all models and soils and their statistical properties such as average, standard deviation, minimum and maximum for all models, the amount of models’ fitting parameters and their statistical properties for all soil samples, soil water retention curve by Arya and Paris (1981) and Meskini-Vishkaee et al. (2014) methods, soil hydraulic conductivity by Arya et al. (1999) method, different textural and hydraulic properties, specific surface area, and other descriptive statistics of PSD for all soil samples. All calculated parameters are presented in an output Excel file format by the software. The software runs under Windows XP/7/8/10.

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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