Preprints
https://doi.org/10.5194/tc-2022-149
https://doi.org/10.5194/tc-2022-149
 
07 Sep 2022
07 Sep 2022
Status: this preprint is currently under review for the journal TC.

Allometric scaling of retrogressive thaw slumps

Jurjen van der Sluijs1, Steven V. Kokelj2, and Jon F. Tunnicliffe3 Jurjen van der Sluijs et al.
  • 1Northwest Territories Centre for Geomatics, Yellowknife, NT, X1A 2L9, Canada
  • 2Northwest Territories Geological Survey, Yellowknife, NT, X1A 2L9, Canada
  • 3School of Environment, University of Auckland, Auckland, NZ

Abstract. In the warming Arctic, retrogressive thaw slumping (RTS) has emerged as the primary thermokarst modifier of ice-rich permafrost slopes, raising urgency to investigate the distribution and intensification of disturbances and to determine trajectories of landscape evolution and the cascade of effects. Tracking RTS is challenging due to constraints of remote sensing products and a narrow understanding of thaw-driven landforms, however, high-resolution elevation models provide new insights into geomorphic change. Structural traits, such as RTS depth-of-thaw or volume, can be obtained through allometric scaling. To address fundamental knowledge gaps related to area-volume scaling of RTS, a suitable surface interpolation technique was first needed to model pre-disturbance topography upon which volume estimates could be based. Among 8 methods with 32 parameterizations, Natural Neighbour surface interpolation achieved the best precision in reconstructing pre-disturbed slope topography (90th percentile Root Mean Square Difference ± 1.0 m). An inverse association between RTS volume and relative volumetric error was observed, with uncertainties <10 % for large slumps and <20 % for small-to-medium slumps. Second, a Multisource Slump Inventory (MSI) for two study areas in the Beaufort Delta (Canada) was required to characterize the diverse range of disturbance morphologies and activity levels, which provided temporally consistent information on thaw slump affected slopes and attributes. The MSI delineation of three high-resolution hillshade DEMs (airborne stereo-imagery, LiDAR, ArcticDEM) revealed temporal and spatial trends in these multi-year, chronic mass-wasting features. For example, in the Tuktoyaktuk Coastal Plains, a +38 % increase in active RTS and +69 % increase in total active surface area were observed between 2004 and 2016. However, the total area of RTS did not change considerably (+3.5 %) because the vast majority of active thaw slumping processes have occurred in association with past disturbances. Interpretation of thaw-driven change is thus dependent on how active RTS are defined to support disturbance inventories. Third, the pre-disturbance topographies, MSI digitizations, and DEMs were integrated to explore allometric scaling relationships between RTS area and eroded volume. The power-law model indicated non-linearity in the rates of RTS expansion and intensification across scale (adj-R2 of 0.85, n=1,522), but also revealed that elongated, shoreline RTS reflects outliers poorly represented by the modelling. This study highlights the importance of linking field-based knowledge to feature identification and the utility of high-resolution DEMs in quantifying rates of RTS erosion beyond tracking change in the planimetric area. Observations further suggested variation in depth-scaling of RTS populations is based on morphometry, terrain position, and complexity of the disturbance area, as well as the method and ontology by which slumps are inventoried.

Jurjen van der Sluijs et al.

Status: open (until 02 Nov 2022)

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Jurjen van der Sluijs et al.

Jurjen van der Sluijs et al.

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Short summary
There is an urgent need to obtain size and erosion estimates of climate-driven landslides, such as retrogressive thaw slumps. We evaluated surface interpolation techniques to estimate slump erosional volumes and developed a new inventory method by which the size and activity of these landslides are tracked through time. Models between slump area and volume reveal nonlinear intensification, whereby model coefficients improve our understanding of how permafrost landscapes may evolve over time.