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

• Main Authors: Cazaurang, Simon, Marcoux, Manuel, Pokrovsky, Oleg S., Loiko, Sergey V., Lim, Artem G., Audry, Stéphane, Shirokova, Liudmila S., Orgogozo, Laurent
• Format: Text
• Language: English
• Published: 2023
• Subjects: Arctic; permafrost
• Online Access: https://doi.org/10.5194/egusphere-2022-475; https://egusphere.copernicus.org/preprints/2022/egusphere-2022-475/

Due to its insulating and draining role, assessing ground vegetation cover properties is important for high-resolution hydrological modeling of permafrost regions. In this study, morphological and effective hydraulic properties of Western Siberian Lowland ground vegetation samples (lichens, Sphagnum mosses, peat) are numerically studied based on tomography scans. Porosity is estimated through a void voxels counting algorithm, showing the existence of representative elementary volumes (REVs) of porosity for most samples. Then, two methods are used to estimate hydraulic conductivity depending on the sample's homogeneity. For homogeneous samples, direct numerical simulations of a single-phase flow are performed, leading to a definition of hydraulic conductivity related to a REV, which is larger than those obtained for porosity. For heterogeneous samples, no adequate REV may be defined. To bypass this issue, a pore network representation 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 for a specific surface area. Hydraulic conductivity fluctuates by 2 orders of magnitude, depending on the method used. Porosity values are in line with previous values found in the literature, showing that arctic cryptogamic cover can be considered 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. However, the uncertainty is less than in experimental studies available in the literature. Therefore, biological and sampling artifacts are predominant over numerical biases. This could be related to compressibility effects occurring during field or laboratory measurements. These numerical methods lay a solid foundation for interpreting the homogeneity of any type of sample and ...