Pathways of ice-wedge degradation in polygonal tundra under different hydrological conditions

Abstract

Abstract. Ice-wedge polygons are common features of lowland tundra in the continuous permafrost zone and prone to rapid degradation through melting of ground ice. There are many inter-related processes involved in ice-wedge thermokarst and it is a major challenge to quantify their influence on the stability of the permafrost underlying the landscape. In this study we used a numerical modelling approach to investigate the degradation of ice-wedges with a focus on the influence of hydrological conditions. Our study area was Samoylov Island in the Lena River delta of Northern Siberia, for which we had in-situ measurements to evaluate the model. The tailored version of the CryoGrid3 Land Surface Model was capable of simulating the changing micro-topography of polygonal tundra and also regarded lateral fluxes of heat, water, and snow. We demonstrated that the approach is capable of simulating ice-wedge degradation and the associated transition from a low-centred to a high-centred polygonal micro-topography. The model simulations showed ice-wedge degradation under recent climatic conditions of the study area, irrespective of hydrological conditions. However, we found that wetter conditions lead to an earlier onset of degradation and cause more rapid ground subsidence. We set our findings in correspondence to observed types of ice-wedge polygons in the study area and hypothesized on remaining discrepancies between modelled and observed ice-wedge thermokarst activity. Our quantitative approach provides a valuable complement to previous, more qualitative and conceptual, descriptions of the possible pathways of ice-wedge polygon evolution. We concluded that our study is a blueprint for investigating thermokarst landforms and marks a step forward in understanding the complex interrelationships between various processes shaping ice-rich permafrost landscapes.

Thaw processes in ice-rich permafrost landscapes represented with laterally coupled tiles in a land surface model

“…4b and c) and mimic the observed conditions more closely (see Boike et al, 2013, 2018. The simulated maximum snow depths in 2008 compare well with observations for both RIM (0.23 m compared to 0.16 m) and the centers (0.39 m compared to 0.46 m), although the observa- tions show a considerable spread (see Nitzbon et al, 2018). This was partly achieved by applying a scaling factor for precipitation (P scale ) of 0.6 ( Table 2).…”

“…1b;Boike et al, 2013Boike et al, , 2018). All degradation stages described by Liljedahl et al (2016) can be found here, from non-degraded LCPs to HCPs with connected troughs (see Nitzbon et al, 2018). Between 1997 and 2017, the mean annual air temperature at the island was approximately −12.3 • C, with an annual liquid precipitation of 169 mm and mean end-of-winter snow depth of 0.3 m (Boike et al, 2018).…”

“…In its default configuration the Noah-MP model (Niu et al, 2011) simulates soil temperature and frozen and liquid water in four soil layers down to a depth of 2 m. It includes up to three snow layers with a representation of liquid water retention and refreezing, as well as a separate canopy layer. Compared to the original Noah code, Noah-MP is an augmented version that includes multiple alternative model representations for key processes, including parameterizations of supercooled liquid water and frozen ground hydraulic conductivity (see details in Niu et al, 2011). It is substantially less complex and computationally expensive than LSMs used in current state-of-the-art ESMs, lacking for instance representations of biogeochemical processes and dynamical vegetation.…”

“…These are in reality different sizes and shapes ( Fig. 1) but can to a first approximation be considered a self-repeating pattern, as also described by Nitzbon et al (2018). Due to symmetry, a larger region can then be represented as a single feature with a representative geometry, neglecting the interaction between different polygons.…”

“…Due to symmetry, a larger region can then be represented as a single feature with a representative geometry, neglecting the interaction between different polygons. For simplicity, here we simulate a representative polygon as a circular feature with a center and rim of equal area and a total diameter of 10 m. Assuming hexagons instead of circles, like Nitzbon et al (2018), would only require minor modifications to the parameters shown in Fig. 3, particularly the distance parameter and the interaction length.…”