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Abstract The 2015 spring flood of the Sagavanirktok River inundated large swaths of tundra as well as infrastructure near Prudhoe Bay, Alaska. Its lasting impact on permafrost, vegetation, and hydrology is unknown but compels attention in light of changing Arctic flood regimes. We combined InSAR and optical satellite observations to quantify subdecadal permafrost terrain changes and identify their controls. While the flood locally induced quasi‐instantaneous ice‐wedge melt, much larger areas were characterized by subtle, spatially variable post‐flood changes. Surface deformation from 2015 to 2019 estimated from ALOS‐2 and Sentinel‐1 InSAR varied substantially within and across terrain units, with greater subsidence on average in flooded locations. Subsidence exceeding 5 cm was locally observed in inundated ice‐rich units and also in inactive floodplains. Overall, subsidence increased with deposit age and thus ground ice content, but many flooded ice‐rich units remained stable, indicating variable drivers of deformation. On average, subsiding ice‐rich locations showed increases in observed greenness and wetness. Conversely, many ice‐poor floodplains greened without deforming. Ice wedge degradation in flooded locations with elevated subsidence was mostly of limited intensity, and the observed subsidence largely stopped within 2 years. Based on remote sensing and limited field observations, we propose that the disparate subdecadal changes were influenced by spatially variable drivers (e.g., sediment deposition, organic layer), controls (ground ice and its degree of protection), and feedback processes. Remote sensing helps quantify the heterogeneous interactions between permafrost, vegetation, and hydrology across permafrost‐affected fluvial landscapes. Interdisciplinary monitoring is needed to improve predictions of landscape dynamics and to constrain sediment, nutrient, and carbon budgets. 

Abstract This study applies an indicators framework to investigate climate drivers of tundra vegetation trends and variability over the 1982–2019 period. Previously known indicators relevant for tundra productivity (summer warmth index (SWI), coastal spring sea-ice (SI) area, coastal summer open-water (OW)) and three additional indicators (continentality, summer precipitation, and the Arctic Dipole (AD): second mode of sea level pressure variability) are analyzed with maximum annual Normalized Difference Vegetation Index (MaxNDVI) and the sum of summer bi-weekly (time-integrated) NDVI (TI-NDVI) from the Advanced Very High Resolution Radiometer time-series. Climatological mean, trends, and correlations between variables are presented. Changes in SI continue to drive variations in the other indicators. As spring SI has decreased, summer OW, summer warmth, MaxNDVI, and TI-NDVI have increased. However, the initial very strong upward trends in previous studies for MaxNDVI and TI-NDVI are weakening and becoming spatially and temporally more variable as the ice retreats from the coastal areas. TI-NDVI has declined over the last decade particularly over High Arctic regions and southwest Alaska. The continentality index (CI) (maximum minus minimum monthly temperatures) is decreasing across the tundra, more so over North America than Eurasia. The relationship has weakened between SI and SWI and TI-NDVI, as the maritime influence of OW has increased along with total precipitation. The winter AD is correlated in Eurasia with spring SI, summer OW, MaxNDVI, TI-NDVI, the CI and total summer precipitation. This winter connection to tundra emphasizes the role of SI in driving the summer indicators. The winter (DJF) AD drives SI variations which in turn shape summer OW, the atmospheric SWI and NDVI anomalies. The winter and spring indicators represent potential predictors of tundra vegetation productivity a season or two in advance of the growing season. 

Much of the Arctic tundra is underlain by a network of ice wedges that formed during millennia of repeated frost cracking on cold winter days and later infilling of snowmelt water. Growing ice wedges push the soil upwards, forming connected ridges on the ground surface and the ubiquitous ice-wedge polygon tundra. Melting of the top of the ice wedge causes the ground surface to collapse with the rims transforming into snow- and water-collecting troughs — a phenomenon observed at multiple sites across the Arctic tundra in a decade or less. Continued melt establishes a new drainage network only a metre or two wide and less than a half-metre deep, where a doubling of runoff and reduced surface water storage is possible without changes in precipitation. Across the Arctic, lakes are disappearing, while precipitation and river runoff are increasing. So far, the sub-metre microtopographical changes have not entered the scientific analyses encompassing regional and pan-Arctic hydrology. The data and technology are now here to quantify the network of ice wedges across large regions and, though individually small, the ice wedges add up to large numbers. What at first may appear as contradicting hydrological change (for example, shrinking lakes despite increasing precipitation) could be explained by a sudden evolution of the stream network where the new channels are narrow but bountiful: the capillaries of the Arctic tundra hydrological system. 

Long-term permafrost observatories are needed to document and monitor rapid changes to ice-rich permafrost systems (IRPS) in a variety of geological, climatic, and infrastructure settings. As part of the US National Science Foundation’s Navigating the New Arctic (NNA) Program, a new observatory was established near the Deadhorse Airport in the eastern part of the Prudhoe Bay Oilfield (PBO) in 2020–23. The NNA-IRPS project has three main research themes: (1) evolution of and degradation of ground ice within the major surficial-geology units; (2) rapid changes in permafrost, landforms, and vegetation due to infrastructure and climate change; and (3) ecological landscapes associated with the calcareous fluvial deposits of the Central Arctic Coastal Plain. 

Ice-wedge polygon (IWP) is a landform found in landscapes underlain by permafrost. IWPs form due to the development of ice wedges, where each IWP is bounded by ice wedges. Ice wedges form due to repeated cracking of the soil during winter and by snowmelt water infiltrating into the cracks and freezing. Repeated over thousands of years, the process results in ice wedges several 10s of feet deep. The melting of the top of the ice wedge results in ground subsidence and depending how extensive the thaw is across the landscape, new ponds or lateral drainage channels form. This data collection supported an assessment of the length of the ice wedge network in the Barnard River watershed (10,540 km2), Banks Island, Canada. The data collection is derived from the pan-Arctic map of ice-wedge polygons (Witharana et al. 2023, Ice-wedge polygon detection in satellite imagery from pan-Arctic regions, Permafrost Discovery Gateway, 2001-2021. Arctic Data Center. doi:10.18739/A2KW57K57), which used Maxar satellite imagery from 2010-2020 for Banks Island. Two types of datasets are included: (1) Polyline shapefile of mapped ice wedge centerlines. This dataset was produced with an approach adopted from Ulrich, Mathias, et al. "Quantifying wedge‐ice volumes in Yedoma and thermokarst basin deposits." Permafrost and Periglacial Processes 25.3 (2014): 151-161. A buffer that represents widths at the top of ice wedges is created around each IWP. A buffer width of 5 meters was chosen, since this allowed buffers of adjacent polygons to overlap. These buffers are then skeletonized in order to trace their centerlines, which ultimately represents the network of ice-wedges that form the IWPs in a landscape. (2) Polygon shapefile of IWP coverage (as percentage of land cover within 1 kilometer (km) x 1 km rectangular grid cells) across the 10,540 km2 Bernard River Watershed, Banks Island, Canada. Code for ice-wedge centerline extraction can be found at https://github.com/PermafrostDiscoveryGateway/IW-Network-Extraction. This data collection accompanies the manuscript published in Nature Water (Liljedahl, A.K., Witharana, C., and Manos, E., 2024. The Capillaries of the Arctic Tundra. Nature Water, doi:10.1038/s44221-024-00276-9) and the geospatial data is available to view in the Permafrost Discovery Gateway. 

The Native Village of Point Lay (Kali) on the North Slope of Alaska has been identified as the second-most permafrost thaw-affected community in the state of Alaska (Denali Commission, 2019). The village has 82 residential units, housing a population of approximately 330. There are several North Slope Borough municipal structures and the Kali School that serve the community. Most of the residential buildings in the village are built on an elevated surface underlain by ice-rich permafrost that is susceptible to thaw and terrain subsidence. This dataset consists of an orthomosaic and digital surface model (DSM) derived from drone surveys on 26 June 2022 in Point Lay, Alaska. 990 digital images were acquired from a DJI Phantom 4 Real-Time Kinematic (DJI P4RTK) quadcopter with a DJI D-RTK 2 Mobile Base Station. The mapped area was around 130 hectares (ha). The drone system was flown at 120 meters (m) above ground level (agl) and flight speeds varied from 7–8 meters/second (m/s). The orientation of the camera was set to 90 degrees (i.e. looking straight down). The along-track overlap and across-track overlap of the mission were set at 80 percent (%) and 70%, respectively. All images were processed in the software Pix4D Mapper (v. 4.7.5) using the standard 3D Maps workflow and the accurate geolocation and orientation calibration method to produce the orthophoto mosaic and digital surface model at spatial resolutions of 5 and 10 centimeters (cm), respectively. A Leica Viva differential global positioning system (GPS) provided ground control for the mission and the data were post-processed to WGS84 UTM Zone 5 North in Ellipsoid Heights (meters). Elevation information derived over waterbodies is noisy and does not represent the surface elevation of the feature. 

Ice-rich permafrost is ground that is frozen all year round for two or more years and contains particularly large amounts of water that will be released upon thawing. This ice is the element of Arctic landscapes most susceptible to climate warming. Nearly 50% of the Arctic has ice-rich permafrost. For example, the upper 4-5 meters of the land along Alaska's northern coast contains an estimated 77% ice. Thawing of ice-rich permafrost affects entire arctic ecosystems and makes the ground unstable to build upon. This dataset consists of an orthomosaic and digital surface model (DSM) derived from drone surveys on 29 August 2021 at the Navigating the New Arctic, Ice-rich Permafrost Systems project field sites, in collaboration with the PermaSense project, in the Prudhoe Bay Oilfields. 2,463 digital images were acquired from a DJI Phantom 4 Real-Time Kinematic (DJI P4RTK) quadcopter with a DJI D-RTK 2 Mobile Base Station. The mapped area was around 232 hectares (ha). The drone system was flown at 100 meters (m) above ground level (agl) and flight speeds varied from 7–8 meters/second (m/s). The orientation of the camera was set to 90 degrees (i.e. looking straight down). The along-track overlap and across-track overlap of the mission were set at 80% and 70%, respectively. All images were processed in the software Pix4D Mapper (v. 4.6.4) using the standard 3D Maps workflow and the accurate geolocation and orientation calibration method to produce the orthophoto mosaic and digital surface model at spatial resolutions of 5 and 10 centimeters (cm), respectively. A Leica Viva differential global positioning system (GPS) provided ground control for the mission and the data were post-processed to WGS84 UTM Zone 6 North in Ellipsoid Heights (meters). Elevation information derived over waterbodies is noisy and does not represent the surface elevation of the feature. 

Land-based transport corridors and related infrastructure are increasingly extending into and across the Arctic in support of resource development and population growth, causing large-scale cumulative changes to northern socio-ecological systems. These changes include the increased mobility of people, goods and resources, and environmental impacts on landscapes and ecosystems as the human footprint reaches remote, unindustrialized regions. Arctic climate change is also generating new challenges for the construction and maintenance of these transport systems, requiring adaptive engineering solutions as well as community resilience. In this review article, we consider the complex entanglements between humans, the environment, and land transportation infrastructure in the North and illustrate these interrelations by way of seven case studies: the Baikal–Amur Mainline, Bovanenkovo Railway, Alaska–Canada Highway, Inuvik–Tuktoyatuk Highway, Alaska Railroad, Hudson Bay Railway, and proposed railways on Baffin Island, Canada. As new infrastructure is built and anticipated across the circumpolar North, there is an urgent need for an integrated socio-ecological approach to impact assessment. This would include full consideration of Indigenous knowledge and concerns, collaboration with local communities and user groups in assessment, planning and monitoring, and evaluation of alternative engineering designs to contend with the impacts of climate change in the decades ahead. 

Increased industrial development in the Arctic has led to a rapid expansion of infrastructure in the region. Localized impacts of infrastructure on snow distribution, road dust, and snowmelt timing and duration feeds back into the coupled Arctic system causing a series of cascading effects that remain poorly understood. We quantify spatial and temporal patterns of snow-off dates in the Prudhoe Bay Oilfield, Alaska, using Sentinel-2 data. We derive the Normalized Difference Snow Index to quantify snow persistence in 2019–2020. The Normalized Difference Vegetation Index and Normalized Difference Water Index were used to show linkages of vegetation and surface hydrology, in relationship to patterns of snowmelt. Newly available infrastructure data were used to analyze snowmelt patterns in relation infrastructure. Results show a relationship between snowmelt and distance to infrastructure varying by use and traffic load, and orientation relative to the prevailing wind direction (up to 1 month difference in snow-free dates). Post-snowmelt surface water area showed a strong negative correlation (up to −0.927) with distance to infrastructure. Results from field observations indicate an impact of infrastructure on winter near-surface ground temperature and snow depth. This study highlights the impact of infrastructure on a large area beyond the direct human footprint and the interconnectedness between snow-off timing, vegetation, surface hydrology, and near-surface ground temperatures. 

Ground ice content of the Arctic soils largely dictates the effects of climate change-induced permafrost degradation and top ground destabilization. The current circumarctic information on ground ice content is overly coarse for many key applications, including assessments of hazards to Arctic infrastructure, while detailed data are restricted to very few regions. This study aims to address these gaps by presenting spatially comprehensive data on pore and segregated ground ice content across the Northern Hemisphere permafrost region at a 1-km resolution. First, ground ice content datasets (n=437 and 380 1-km grid cells for volumetric and gravimetric ice content, respectively) were compiled from field observations over the permafrost region. Spatial estimates of ground ice content in the near-surface permafrost north of the 30th parallel north were then produced by relating observed ground ice content to physically relevant environmental data layers of climate, soil, topography, and vegetation properties using a statistical modelling framework. The produced data show that ground ice content varies substantially across the permafrost region. The highest ice contents are found on peat-dominated Arctic lowlands and along major river basins. Low ice contents are associated with mountainous areas and many sporadic and isolated permafrost regions. The modelling yields relatively small prediction errors (a mean absolute error of 13.6 % volumetric ice content) over evaluation data and broadly congruent spatial distributions with earlier regional-scale studies. The presented data allow the consideration of ground ice content in various geomorphological, ecological, and environmental impact assessment applications at a scale that is more relevant than previous products. The produced ground ice data are available in the supplement for this study and at Zenodo https://doi.org/10.5281/zenodo.7009875 (Karjalainen et al., 2022).