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Modeling Structural Influences on Soil Water Retention
Author(s) -
Nimmo John R.
Publication year - 1997
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj1997.03615995006100030002x
Subject(s) - soil texture , texture (cosmology) , soil structure , component (thermodynamics) , aggregate (composite) , porosity , characterisation of pore space in soil , particle (ecology) , water retention , particle size distribution , soil science , range (aeronautics) , statistical physics , particle size , mathematics , mineralogy , soil water , environmental science , geology , materials science , geotechnical engineering , computer science , physics , thermodynamics , artificial intelligence , composite material , image (mathematics) , paleontology , oceanography
Abstract A new model quantifies the effect of soil structure, considered as the arrangement of particles in the soil, on soil water retention. The model partitions the pore space into texture‐related and structure‐related components, the textural component being what can be deduced to exist if the arrangement of the particles were random, and the structural component being the remainder. An existing model, based on particle‐size distributions, represents the textural component, and a new model, based on aggregate‐size distributions, represents the structural component. This new model makes use of generalized properties that vary little from one medium to another, thereby eliminating any need for empirically fitted parameters. It postulates a particular character of the structural pore space that in some ways resembles texture‐related pore space, but with pore shape related to the breadth of the aggregate‐size distribution. To predict a soil water retention curve, this model requires the soil's porosity and particle‐ and aggregate‐size distributions. Tested with measurements for 17 samples from two sources, it fits the data much better than does a model based on texture alone. Goodness of fit indicated by correlation coefficients ranged from 0.908 to 0.998 for the new model, compared with a range of 0.686 to 0.955 for the texture‐based model.