Preprints
https://doi.org/10.5194/egusphere-2022-475
https://doi.org/10.5194/egusphere-2022-475
 
12 Jul 2022
12 Jul 2022

Numerical Assessment of Morphological and Hydraulic Properties of Moss, Lichen and Peat from a Permafrost Peatland

Simon Cazaurang1, Manuel Marcoux1, Oleg S. Pokrovsky2,3, Sergey V. Loiko3, Artem G. Lim3, Stéphane Audry2, Liudmila S. Shirokova2,4, and Laurent Orgogozo2 Simon Cazaurang et al.
  • 1Toulouse Institute of Fluid Mechanics (IMFT), National Polytechnic Institute of Toulouse, Toulouse, F-31400, France
  • 2Geosciences Environnement Toulouse (GET) Laboratory, University Toulouse III – Paul Sabatier, Toulouse, F-31400, France
  • 3BIO-GEO-CLIM Laboratory, Tomsk State University, Tomsk, Russian Federation
  • 4N. Laverov Federal Center for Integrated Arctic Research of the Ural Branch – Russian Academy of Science, Russian Federation

Abstract. The hydraulic properties of ground vegetation cover are important for high resolution hydrological modeling of permafrost regions, due to its insulating and draining role. In this study, the morphological and effective hydraulic properties of Western Siberian Lowland ground vegetation samples (lichens, Sphagnum mosses, peat) are numerically assessed based on tomography scans. After numerical pre-processing, porosity is estimated through a void voxels counting algorithm, showing the existence of representative elementary volumes (REV) of porosity for most samples. Then, two methods are used to estimate hydraulic conductivity depending on the sample’s homogeneity. For the most homogeneous samples, Direct Numerical Simulations (DNS) of a single-phase flow are performed, leading to a definition of hydraulic conductivity related to REV, which is larger than those obtained for porosity. For more heterogeneous samples, no adequate REV may be defined. To bypass this issue, a pore network representation of the whole sample is created from computerized scans. Morphological and hydraulic properties are then estimated through this simplified representation. Both methods converged on similar results for porosity. Some discrepancies are observed in the morphological properties (specific surface area). Hydraulic conductivity fluctuates by two orders of magnitude, depending on the method used, and yet this uncertainty is less than that found in experimental studies. Therefore, biological and sampling artifacts are predominant over numerical biases. Porosity values are in line with previous values found in the literature, showing that arctic cryptogamic cover can be considered as an open and well-connected porous medium (over 99 % of overall porosity is open porosity). Meanwhile, digitally estimated hydraulic conductivity is higher compared to previously obtained results based on field and laboratory experiments. This could be related to compressibility effects, occurring during field or laboratory measurements. Thus, some supplementary studies are compulsory for assessing syn-sampling and syn-measurement perturbations in experimentally estimated, effective hydraulic properties of such a biological porous medium.

Simon Cazaurang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-475', Xiaoying Zhang, 24 Aug 2022
    • AC1: 'Reply on RC1', Simon Cazaurang, 08 Nov 2022
  • RC2: 'Comment on egusphere-2022-475', Anonymous Referee #2, 23 Oct 2022
    • AC2: 'Reply on RC2', Simon Cazaurang, 08 Nov 2022

Simon Cazaurang et al.

Simon Cazaurang et al.

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Short summary
Moss, lichen and peat samples are reconstructed using X-ray tomography. Most samples can be cut down to a representative volume based on porosity. However, only homogeneous samples could be reduced to a representative volume based on hydraulic conductivity. For heterogeneous samples, a devoted pore network model is computed. The studied samples are mostly highly porous and water-conductive. These results must be put into perspective with compressibility phenomena occurring in field tests.