Description: Roads constructed on permafrost can have a significant impact on the surrounding environment, potentially inducing permafrost degradation. These impacts arise from factors such as snow accumulation near the road, which affects the soil's thermal and hydrological regime, and road dust that decreases the snow's albedo, altering the timing of snowmelt. However, our current understanding of the magnitude and the spatial extent of these effects is limited. In this study we addressed this gap by using remote sensing techniques to assess the spatial effect of the Inuvik to Tuktoyaktuk Highway (ITH) in Northwest Territories, Canada, on snow accumulation, snow albedo and snowmelt patterns. With a new, high resolution snow depth raster from airborne laser scanning, we quantified the snow accumulation at road segments in the Trail Valley Creek area using digital elevation model differencing. We found increased snow accumulation up to 36 m from the road center. The magnitude of this snow accumulation was influenced by the prevailing wind direction and the embankment height. Furthermore, by analyzing 43 Sentinel-2 satellite images between February and May 2020, we observed reduced snow albedo values within 500 m of the road, resulting in a 12-days-earlier onset of snowmelt within 100 m from the road. We examined snowmelt patterns before, during and after the road construction using the normalized difference snow index from Landsat-7 and Landsat-8 imagery. Our analysis revealed that the road affected the snowmelt pattern up to 600 m from the road, even in areas which appeared undisturbed. In summary, our study improves our understanding of the spatial impact of gravel roads on permafrost due to enhanced snow accumulation, reduced snow albedo and earlier snowmelt. Our study underscores the important contribution that remote sensing can provide to improve our understanding of the effects of infrastructure development on permafrost environments.

Description: Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

Description: Warming induced shifts in tundra vegetation composition and structure, including circumpolar expansion of shrubs, modifies ecosystem structure and functioning with potentially global consequences due to feedback mechanisms between vegetation and climate. Satellite-derived vegetation indices indicate widespread greening of the surface, often associated with regional evidence of shrub expansion obtained from long-term ecological monitoring and repeated orthophotos. However, explicitly quantifying shrub expansion across large scales using satellite observations requires characterising the fine-scale mosaic of Arctic vegetation types beyond index-based approaches. Although previous studies have illustrated the potential of estimating fractional cover of various Plant Functional Types (PFTs) from satellite imagery, limited availability of reference data across space and time has constrained deriving fraction cover time series capable of detecting shrub expansion. We applied regression-based unmixing using synthetic training data to build multitemporal machine learning models in order to estimate fractional cover of shrubs and other surface components in the Mackenzie Delta Region for six time intervals between 1984 and 2020. We trained Kernel Ridge Regression (KRR) and Random Forest Regression (RFR) models using Landsat-derived spectral-temporal-metrics and synthetic training data generated from pure class spectra obtained directly from the imagery. Independent validation using very-high-resolution imagery suggested that KRR outperforms RFR, estimating shrub cover with a MAE of 10.6 and remaining surface components with MAEs between 3.0 and 11.2. Canopy-forming shrubs were well modelled across all cover densities, coniferous tree cover tended to be overestimated and differentiating between herbaceous and lichen cover was challenging. Shrub cover expanded by on average + 2.2 per decade for the entire study area and + 4.2 per decade within the low Arctic tundra, while relative changes were strongest in the northernmost regions. In conjunction with shrub expansion, we observed herbaceous plant and lichen cover decline. Our results corroborate the perception of the replacement and homogenisation of Arctic vegetation communities facilitated by the competitive advantage of shrub species under a warming climate. The proposed method allows for multidecadal quantitative estimates of fractional cover at 30 m resolution, initiating new opportunities for mapping past and present fractional cover of tundra PFTs and can help advance our understanding of Arctic shrub expansion within the vast and heterogeneous tundra biome.

Description: Svalbard is a hotspot of climate change in the rapidly warming Arctic. The strong air temperature warming coincides with a multitude of changes in other climate variables such as liquid precipitation, snow cover, and the surface energy budget components. These changes have highly complex effects on the soil temperature and freezing conditions. We investigate seasonal patterns of change in climate and soil conditions at the Bayelva study site close to Ny-Ålesund, Svalbard for the period 1998-2020. We use Bayesian inference to estimate trends in monthly mean values of air and soil temperature, radiation fluxes, sensible and latent heat flux, liquid precipitation, snow depth, and soil moisture. We then apply PCMCI+, a recently developed causal inference framework, in order to quantify the contributions of all meteorological variables to soil warming. Air temperature at the Bayelva site rose in all months of the year in the last 23 years (1998-2020). This trend has been particularly strong in April (1.3°C/10years), September (1.5°C/10years) and October (1.9°C/10years). The strong changes in spring and autumn led to earlier snowmelt (-14 days/10 years, 2007-2020) and more snow free days (+26 days/10years, 2007-2020). We observe later soil freezing in October and lower snow depth. Furthermore, strong rain events have become more frequent in winter, which contributed to soil warming. As a result of changes in air temperature, water fluxes, and the energy budget, top soil temperature increased in particular during spring (May/June 1.4°C/10years, 1998-2020). Our results illustrate how rapid climate change drives soil warming and permafrost thaw. They can help to validate results from climate and land surface models as well as aid in future predictions of landscape changes in Svalbard.

Description: Comprehensive metadata are key to making data FAIR. It is therefore essential to collect metadata in an organized and standardized way. For standardized data acquisition, ie. on research vessels, tools are already available and constantly improved. In land-based permafrost expeditions, however, the data and metadata are as diverse as the science questions behind them. We present an overview of this diversity in (meta)data and (meta)data collection and propose strategies to writing good and comprehensive metadata. We encourage to think about metadata right from the start and work on them steadily during the whole process from field work preparation to data collection and from data analysis to final publication. Easy to adapt templates and only choosing the tools that fit the specific data set increases the participation of the whole team.

Description: This data set contains quality controlled soil temperature and volumetric liquid water content measurements from a site at the forest-tundra transition in the western Canadian Arctic. The measurement site is near by the Trail Valley Creek (TVC) research station, which is operated by Wilfried Laurier University. The station is located east of the Mackenzie Delta and 45 km north of Inuvik. The published data set provides long-term observations on the thermal and hydrological state of the active layer at a hummocky tundra site within the continuous permafrost zone. The station consists of a soil depth-profile with four sensor sets at 2cm, 5cm, 10cm, and 20cm of paired soil moisture and soil temperature sensors. Additionally, three distributed soil moisture sensors are installed in the surface soil. The data series is continued since 2016 and provides a basis for process understanding and modelling studies of Arctic permafrost.

Description: This data set contains quality controlled soil temperature and volumetric liquid water content measurements from a site at the forest-tundra transition in the western Canadian Arctic. The measurement site is near by the Trail Valley Creek (TVC) research station, which is operated by Wilfried Laurier University. The station is located east of the Mackenzie Delta and 45 km north of Inuvik. The published data set provides long-term observations on the thermal and hydrological state of the active layer at a hummocky tundra site within the continuous permafrost zone. The station consists of a soil depth-profile with four sensor sets at 2cm, 5cm, 10cm, and 20cm of paired soil moisture and soil temperature sensors. Additionally, three distributed soil moisture sensors are installed in the surface soil. The data series is continued since 2016 and provides a basis for process understanding and modelling studies of Arctic permafrost.

Description: This data set contains quality controlled soil temperature and volumetric liquid water content measurements from a site at the forest-tundra transition in the western Canadian Arctic. The measurement site is near by the Trail Valley Creek (TVC) research station, which is operated by Wilfried Laurier University. The station is located east of the Mackenzie Delta and 45 km north of Inuvik. The published data set provides long-term observations on the thermal and hydrological state of the active layer at a hummocky tundra site within the continuous permafrost zone. The station consists of a soil depth-profile with four sensor sets at 2cm, 5cm, 10cm, and 20cm of paired soil moisture and soil temperature sensors. Additionally, three distributed soil moisture sensors are installed in the surface soil. The data series is continued since 2016 and provides a basis for process understanding and modelling studies of Arctic permafrost.

Description: This dataset contains topsoil temperature data of 68 sensors, which were installed at the Arctic tundra site of Trail Valley Creek, Northwest Territories, Canada (133.499 °W, 68.742 °N). The sensors were located below six different vegetation types (trees, tall shrubs, riparian tall shrubs, dwarf shrubs, tussocks and lichen tundra) at a depth of 1 to 5 cm, protected from solar radiation. The sensors were placed between 0.22 m to more than 1 km apart from each other. The mean distance between nearest neighbouring sensors was 10.65 m. The sensor network is designed to monitor the effects of changing surface parameters such as vegetation, micro topography and soil moisture on seasonal thawing and freezing processes and on long-term warming of permafrost temperatures. The topsoil temperature was measured using coated iButton temperature loggers (DS1922L) at 0.0625°C resolution and an accuracy of 0.5° C (Maxim Integrated Products, Inc., 2015). The record covers the two periods from 28th of August 2016, 3:00 to 3rd of September 2017, 15:00 and from 4th of September 2017, 12:00 to 22nd of August 2018, 21:00 (UTC, local time + 7 hours) in 3 hourly resolution. Between the two periods, the sensors were removed, read out, and placed at the same positions again. The two periods were analysed by Grünberg et al., 2020, who describe the measurements in more detail.

Description: This data set contains quality controlled soil temperature and volumetric liquid water content measurements from a site at the forest-tundra transition in the western Canadian Arctic. The measurement site is near by the Trail Valley Creek (TVC) research station, which is operated by Wilfried Laurier University. The station is located east of the Mackenzie Delta and 45 km north of Inuvik. The published data set provides long-term observations on the thermal and hydrological state of the active layer at a hummocky tundra site within the continuous permafrost zone. The station consists of a soil depth-profile with four sensor sets at 2cm, 5cm, 10cm, and 20cm of paired soil moisture and soil temperature sensors. Additionally, three distributed soil moisture sensors are installed in the surface soil. The data series is continued since 2016 and provides a basis for process understanding and modelling studies of Arctic permafrost.