Prediction of the soil water characteristic from soil particle volume fractions

Naveed, Muhammad, Moldrup, Per, Tuller, Markus, Ferrè, T. P. A., Kawamoto, Ken, Komatsu, Toshiko and de Jonge, Lis Wollesen (2012) Prediction of the soil water characteristic from soil particle volume fractions. Soil Science Society of America Journal, 76 (6). p. 1946. ISSN 0361-5995

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Abstract

Modeling water distribution and flow in partially saturated soils requires knowledge of the soil water characteristic (SWC). However, measurement of the SWC is challenging and time-consuming and, in some cases, not feasible. This study introduces two predictive models (FW–model and AW–model) for the SWC, derived from readily available soil properties such as texture and bulk density. A total of 46 undisturbed soils from different horizons at 15 locations across Denmark were used for model evaluation. The FW–model predicts the volumetric water content as a function of volumetric fines content (organic matter and clay). It performed reasonably well for the dry-end of SWC (above a pF value of 2.0; pF = log(|ψ|), where ψ is the matric potential in cm), but did not do as well closer to saturated conditions. The AW–model predicts the volumetric water content as a function of volumetric content of different particle size fractions (organic matter, clay, silt, and fine and coarse sands). The volumetric content of a particular soil particle size fraction was considered if it contributed to the pore size fraction still occupied with water at the given pF value. Hereby, the AW–model implicitly assumes that a given particle size fraction creates an analogue pore size fraction and further this pore size fraction filled with water is corresponding to a certain pF value according to the well-known capillary rise equation. The AW–model was found to be quite robust, and it performed exceptionally well for pF values ranging from 0.4 to 4.2 for different soil types. For prediction of the continuous SWC, it is recommended to parameterize the van Genuchten model based on the SWC data points predicted by the AW–model.

Item Type: Article
Subjects: Construction and engineering > Civil and environmental engineering
Depositing User: Muhammad Naveed
Date Deposited: 17 May 2018 14:59
Last Modified: 18 May 2018 06:56
URI: http://repository.uwl.ac.uk/id/eprint/5005

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