Numerical assessment of morphological and hydraulic properties of moss, lichen and peat from a permafrost peatland

Abstract

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.

Future permafrost degradation under climate change in a headwater catchment of Central Siberia: quantitative assessment with a mechanistic modelling approach

“…The use of mechanistic modelling, although computationally costly, is capable of providing quantitative information for feeding these research fields. This approach will be applied in other environmentally monitored boreal watershed in the near future, in order to numerically characterize the physical response of permafrost to climate change in various representative permafrost context, for instance in Northern Sweden (Auda et al, 2023) and Western Siberia (Cazaurang et al, 2023).…”

“…Indeed, soil temperature and air temperature may be significantly different in such a boreal forest environment, due to the effects of understory (Zellweger et al, 2019, Haesen et al, 2021, moss cover insulation (Blok et al, 2011, Cazaurang et al, 2023, the winter snowpack (Jan andPainter, 2020, Khani et al, 2023) and its interactions with vegetation (Dominé et al, 2022). This empirical, site-specific procedure is detailed in Supplementary material A, and it allows to build up a slopewise soil temperature estimates on the basis of air temperature and snow conditions.…”

“…Here, we simulate, using a mechanistic modelling approach, the permafrost dynamics at the catchment scale until 2100 under various scenarios of climate change. The vegetation controls on permafrost dynamics are partly included in the mechanistic modelling framework, considering evapotranspiration fluxes (Orgogozo et al, 2019), and partly handled empirically, via the accounting on the insulating effect of ground floor vegetation (Blok et al, 2011, Cazaurang et al, 2023. However, because no changes of vegetation is explicitly considered, we assume constant biomass and primary production and therefore investigate only the physical part of the response of permafrost to climate change.…”