Inverse modelling for predicting both water and nitrate movement in a structured-clay soil (Red Ferrosol) CQU.pdf (3.31 MB)

Inverse modelling for predicting both water and nitrate movement in a structured-clay soil (Red Ferrosol)

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Version 2 2022-09-18, 22:43
Version 1 2021-01-17, 10:59
journal contribution
posted on 2022-09-18, 22:43 authored by JM Kirkham, CJ Smith, RB Doyle, Philip BrownPhilip Brown
Soil physical parameter calculation by inverse modelling provides an indirect way of estimating the unsaturated hydraulic properties of soils. However many measurements are needed to provide sufficient data to determine unknown parameters. The objective of this research was to assess the use of unsaturated water flow and solute transport experiments, in horizontal packed soil columns, to estimate the parameters that govern water flow and solute transport. The derived parameters are then used to predict water infiltration and solute migration in a repacked soil wedge. Horizontal columns packed with Red Ferrosol were used in a nitrate diffusion experiment to estimate either three or six parameters of the van Genuchten–Mualem equation while keeping residual and saturated water content, and saturated hydraulic conductivity fixed to independently measured values. These parameters were calculated using the inverse optimisation routines in Hydrus 1D. Nitrate concentrations measured along the horizontal soil columns were used to independently determine the Langmuir adsorption isotherm. The soil hydraulic properties described by the van Genuchten–Mualem equation, and the NO –3 adsorption isotherm, were then used to predict water and NO –3 distributions from a point-source in two 3D flow scenarios. The use of horizontal columns of repacked soil and inverse modelling to quantify the soil water retention curve was found to be a simple and effective method for determining soil hydraulic properties of Red Ferrosols. These generated parameters supported subsequent testing of interactive flow and reactive transport processes under dynamic flow conditions. Copyright 2019 Kirkham et al.


Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)




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PeerJ, UK

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University of Tasmania; CSIRO

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