Prediction of biopore- and matrix-dominated flow from X-ray CT-derived macropore network characteristics

Abstract

Abstract. Prediction and modeling of localized flow processes in macropores is of crucial importance for sustaining both soil and water quality. However, currently there are no reliable means to predict preferential flow due to its inherently large spatial variability. The aim of this study was to investigate the predictive performance of previously developed empirical models for both water and air flow and to explore the potential applicability of X-ray computed tomography (CT)-derived macropore network characteristics. For this purpose, 65 cylindrical soil columns (6 cm diameter and 3.5 cm height) were extracted from the topsoil (5 cm to 8.5 cm depth) in a 15 m × 15 m grid from an agricultural field located in Silstrup, Denmark. All soil columns were scanned with an industrial X-ray CT scanner (129 µm resolution) and later employed for measurement of saturated hydraulic conductivity, air permeability at −30 and −100 cm matric potential, and gas diffusivity at −30 and −100 cm matric potential. Distribution maps for saturated hydraulic conductivity, air permeability, and gas diffusivity reflected no autocorrelation irrespective of soil texture and organic matter content. Existing empirical predictive models for saturated hydraulic conductivity and air permeability showed poor performance, as they were not able to realistically capture macropore flow. The tested empirical model for gas diffusivity predicted measurements at −100 cm matric potential reasonably well, but failed at −30 cm matric potential, particularly for soil columns with biopore-dominated flow. X-ray CT-derived macroporosity matched the measured airfilled porosity at −30 cm matric potential well. Many of the CT-derived macropore network characteristics were strongly interrelated. Most of the macropore network characteristics were also significantly correlated with saturated hydraulic conductivity, air permeability, and gas diffusivity. The predictive Ahuja et al. (1984) model for saturated hydraulic conductivity, air permeability, and gas diffusivity performed reasonably well when parameterized with novel, X-ray CTderived parameters such as effective percolating macroporosity for biopore-dominated flow and total macroporosity for matrix-dominated flow. The obtained results further indicate that it is crucially important to discern between matrixdominated and biopore-dominated flow for accurate prediction of macropore flow from X-ray CT-derived macropore network characteristics.

Conservation Agriculture Had a Poor Impact on the Soil Porosity of Veneto Low‐lying Plain Silty Soils after a 5‐year Transition Period

“…Most likely, the small volume of the soil cores limited the detection of very large pores. The importance of biopores has recently been emphasized by Naveed et al (2016) as they sustain both soil and water quality through structure stabilization and water infiltration and drainage improvements. Indeed, vertical-oriented bio-macropores are resistant under traffic load (Blackwell et al, 1990) and would be expected to extend deeper than in a previously tilled zone, so the benefits associated with biopores could involve the entire soil profile and be essential for no-tillage system on poorly drained and aerated soils.…”

Quantitative evaluation of pore characteristics of sodic soils reclaimed by flue gas desulphurization gypsum using X‐ray computed tomography

“…Previous review articles and research papers have offered sufficient study cases in exploring the reliability of applying X‐ray CT to quantify the soil pore characteristics (Cnudde & Boone, ; Haeffneff, ; Naveed et al, ; Taina et al ., 2008; Zhou, Mooney, & Peng, ). For example, studies have been widely carried out on segmentation of X‐ray CT images (Baveye et al, ; Iassonov, Gebrenegus, & Tuller, ; Schlüter, Sheppard, Brown, & Wildenschild, ) involving image resolution selection (Sleutel et al, ; Wildenschild et al, ), representative elementary area identification (San José Martínez, Caniego, García‐Gutiérrez, & Espejo, ), and optimal thresholding methods (Elliot & Heck, ; Smet, Plougonven, Leonard, Degré, & Beckers, ; Wang, Kravchenko, Smucker, & Rivers, ); quantification and reconstruction of the pore structure (Marcelino, Cnudde, Vansteelandt, & Carò, ) such as characterization of macropores (Garbout, Munkholm, & Hansen, ; Luo, Lin, & Schmidt, ), extraction of three‐dimensional (3D) typical pore parameters (Al‐Raoush & Willson, ; Luo, Lin, & Li, ), and assessing the spatial variability of soil structure (Carducci, Zinn, Rossoni, Heck, & Oliveira, ); as well as the relationship between pore characteristics and soil functions (Helliwell et al, ) including correlations with soil physical properties (Anderson, Gantzer, Boone, & Tully, ; Munkholm, Heck, & Deen, ) and explanation of the hydraulic conductivity (Luo, Lin, & Halleck, ; Naveed et al, ; Paradelo et al, ; Tracy et al, ). Although the majority of current case studies focus on naturally cultivated soils, there are still some reports confirming that images extracted from X‐ray CT are effective in quantifying pore characteristics of unnaturally soils such as reconstructed, degraded, and reclaimed soils (Dowuona, Taina, & Heck, ; Langmaack, Schrader, Rapp‐Bernhardt, & Kotzke, ; Li, Shao, & Jia, ; Wang, Guo, Bai, & Yang, ).…”

A Review of Geophysical Methods for Soil Structure Characterization

“…and (iv) how do these properties evolve over time? Studies suggest that properties that capture the soil structural form (defined here as the pore size distribution, pore connectivity, and pore stability) are important in many dynamic soil processes (Keesstra et al, ; Naveed et al, ; Rabot et al, ; Stewart et al, ). Laboratory measurements and imaging capabilities allow for detailed quantification of soil structure within individual soil samples (Helliwell et al, ; Schlüter et al, ).…”