The overall spatial and temporal influence of shrub expansion on permafrost is largely unknown due to uncertainty in estimating the magnitude of many counteracting processes. For example, shrubs shade the ground during the snow-free season, which can reduce active layer thickness. At the same time, shrubs advance the timing of snowmelt when they protrude through the snow surface, thereby exposing the active layer to thawing earlier in spring. Here, we compare 3056 in situ frost table depth measurements split between mineral earth hummocks and organic inter-hummock zones across four dominant shrub–tundra vegetation types. Snow-free date, snow depth, hummock development, topography, and vegetation cover were compared to frost table depth measurements using a structural equation modeling approach that quantifies the direct and combined interacting influence of these variables. Areas of birch shrubs became snow free earlier regardless of snow depth or hillslope aspect because they protruded through the snow surface, leading to deeper hummock frost table depths. Projected increases in shrub height and extent combined with projected decreases in snowfall would lead to increased shrub protrusion across the Arctic, potentially deepening the active layer in areas where shrub protrusion advances the snow-free date.

Abstract. Connections between vegetation and soil thermal dynamics are critical for estimating the vulnerability of permafrost to thaw with continued climate warming and vegetation changes. The interplay of complex biophysical processes results in a highly heterogeneous soil temperature distribution on small spatial scales. Moreover, the link between topsoil temperature and active layer thickness remains poorly constrained. Sixty-eight temperature loggers were installed at 1–3 cm depth to record the distribution of topsoil temperatures at the Trail Valley Creek study site in the northwestern Canadian Arctic. The measurements were distributed across six different vegetation types characteristic for this landscape. Two years of topsoil temperature data were analysed statistically to identify temporal and spatial characteristics and their relationship to vegetation, snow cover, and active layer thickness. The mean annual topsoil temperature varied between −3.7 and 0.1 ∘C within 0.5 km2. The observed variation can, to a large degree, be explained by variation in snow cover. Differences in snow depth are strongly related with vegetation type and show complex associations with late-summer thaw depth. While cold winter soil temperature is associated with deep active layers in the following summer for lichen and dwarf shrub tundra, we observed the opposite beneath tall shrubs and tussocks. In contrast to winter observations, summer topsoil temperature is similar below all vegetation types with an average summer topsoil temperature difference of less than 1 ∘C. Moreover, there is no significant relationship between summer soil temperature or cumulative positive degree days and active layer thickness. Altogether, our results demonstrate the high spatial variability of topsoil temperature and active layer thickness even within specific vegetation types. Given that vegetation type defines the direction of the relationship between topsoil temperature and active layer thickness in winter and summer, estimates of permafrost vulnerability based on remote sensing or model results will need to incorporate complex local feedback mechanisms of vegetation change and permafrost thaw.

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This paper investigates different methods for quantifying thaw subsidence using terrestrial laser scanning (TLS) point clouds. Thaw subsidence is a slow (millimetre to centimetre per year) vertical displacement of the ground surface common in ice‐rich permafrost‐underlain landscapes. It is difficult to quantify thaw subsidence in tundra areas as they often lack stable reference frames. Also, there is no solid ground surface to serve as a basis for elevation measurements, due to a continuous moss–lichen cover. We investigate how an expert‐driven method improves the accuracy of benchmark measurements at discrete locations within two sites using multitemporal TLS data of a 1‐year period. Our method aggregates multiple experts’ determination of the ground surface in 3D point clouds, collected in a web‐based tool. We then compare this to the performance of a fully automated ground surface determination method. Lastly, we quantify ground surface displacement by directly computing multitemporal point cloud distances, thereby extending thaw subsidence observation to an area‐based assessment. Using the expert‐driven quantification as reference, we validate the other methods, including in‐situ benchmark measurements from a conventional field survey. This study demonstrates that quantifying the ground surface using 3D point clouds is more accurate than the field survey method. The expert‐driven method achieves an accuracy of 0.1 ± 0.1 cm. Compared to this, in‐situ benchmark measurements by single surveyors yield an accuracy of 0.4 ± 1.5 cm. This difference between the two methods is important, considering an observed displacement of 1.4 cm at the sites. Thaw subsidence quantification with the fully automatic benchmark‐based method achieves an accuracy of 0.2 ± 0.5 cm and direct point cloud distance computation an accuracy of 0.2 ± 0.9 cm. The range in accuracy is largely influenced by properties of vegetation structure at locations within the sites. The developed methods enable a link of automated quantification and expert judgement for transparent long‐term monitoring of permafrost subsidence. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd

Arctic tundra environments are characterized by a spatially heterogeneous end-of-winter snow depth resulting from wind transport and deposition. Traditional methods for measuring snow depth do not accurately capture such heterogeneity at catchment scales. In this study we address the use of high-resolution, spatially distributed, snow depth data for Arctic environments through the application of Unmanned Aerial Systems (UAS). We apply Structure-from-Motion photogrammetry to images collected using a fixed-wing UAS to produce a 1-metre resolution snow depth product across seven areas-of-interest (AOIs) within the Trail Valley Creek Research Watershed, Northwest Territories. We evaluated these snow depth products with in situ measurements of both the snow surface elevation (n=8434) and snow depth (n=7191). When all AOIs were averaged, the RMSE of the snow surface elevation models was 0.16 m (<0.01 m bias), similar to the snow depth product (UASsd) RMSE of 0.15 m (+0.04 m bias). The distribution of snow depth between in situ measurements and UASsd was similar along the transects where in situ snow depth was collected, although similarity varies by AOI. Finally, we provide a discussion around factors that may influence the accuracy of the snow depth products including vegetation, environmental conditions, and study design.