Abstract. 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 processing some quantitative properties' assessment, either with image processing or with a pore network
model. The main observed limitation is the input data quality (e.g., the tomographic scans' resolution) and its pre-processing scheme. 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.