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In 2007, the Anaktuvuk River fire burned more than 1000 km²of arctic tundra in northern Alaska, ~ 50% of which occurred in an area with ice-rich syngenetic permafrost (Yedoma). By 2014, widespread degradation of ice wedges was apparent in the Yedoma region. In a 50 km²area, thaw subsidence was detected across 15% of the land area in repeat airborne LiDAR data acquired in 2009 and 2014. Updating observations with a 2021 airborne LiDAR dataset show that additional thaw subsidence was detected in < 1% of the study area, indicating stabilization of the thaw-affected permafrost terrain. Ground temperature measurements between 2010 and 2015 indicated that the number of near-surface soil thawing-degree-days at the burn site were 3 × greater than at an unburned control site, but by 2022 the number was reduced to 1.3 × greater. Mean annual ground temperature of the near-surface permafrost increased by 0.33 °C/yr in the burn site up to 7-years post-fire, but then cooled by 0.15 °C/yr in the subsequent eight years, while temperatures at the control site remained relatively stable. Permafrost cores collected from ice-wedge troughs (n = 41) and polygon centers (n = 8) revealed the presence of a thaw unconformity, that in most cases was overlain by a recovered permafrost layer that averaged 14.2 cm and 18.3 cm, respectively. Taken together, our observations highlight that the initial degradation of ice-rich permafrost following the Anaktuvuk River tundra fire has been followed by a period of thaw cessation, permafrost aggradation, and terrain stabilization.

 

Northern high-latitudes are projected to get warmer and wetter, which will affect rates of permafrost thaw and mechanisms by which thaw occurs. To better understand the impact of rain, as well as other factors such as snow depth, canopy cover, and microtopography, we instrumented a degrading permafrost plateau in south-central Alaska with high-resolution soil temperature sensors. The site contains ecosystem-protected permafrost, which persists in unfavorable climates due to favorable ecologic conditions. Our study (2020–2022) captured three of the snowiest years and three of the four wettest years since the site was first studied in 2015. Average thaw rates along an across-site transect increased nine-fold from 6 ± 5 cm yr⁻¹(2015–2020) to 56 ± 12 cm yr⁻¹(2020–2022). This thaw was not uniform. Hummock locations, residing on topographic high points with relatively dense canopy, experienced only 8 ± 9 cm yr⁻¹of thaw, on average. Hollows, topographic low points with low canopy cover, and transition locations, which had canopy cover and elevation between hummocks and hollows, thawed 44 ± 6 cm yr⁻¹and 39 ± 13 cm yr⁻¹, respectively. Mechanisms of thaw differed between these locations. Hollows had high warm-season soil moisture, which increased thermal conductivity, and deep cold-season snow coverage, which insulated soil. Transition locations thawed primarily due to thermal energy transported through subsurface taliks during individual rain events. Most increases in depth to permafrost occurred below the ∼45 cm thickness seasonally frozen layer, and therefore, expanded existing site taliks. Results highlight the importance of canopy cover and microtopography in controlling soil thermal inputs, the ability of subsurface runoff from individual rain events to trigger warming and thaw, and the acceleration of thaw caused by consecutive wet and snowy years. As northern high-latitudes become warmer and wetter, and weather events become more extreme, the importance of these controls on soil warming and thaw is likely to increase.

 

Wetlands in Arctic drained lake basins (DLBs) have a high potential for carbon storage in vegetation and peat as well as for elevated greenhouse gas emissions. However, the evolution of vegetation and organic matter is rarely studied in DLBs, making these abundant wetlands especially uncertain elements of the permafrost carbon budget. We surveyed multiple DLB generations in northern Alaska with the goal to assess vegetation, microtopography, and organic matter in surface sediment and pond water in DLBs and to provide the first high-resolution land cover classification for a DLB system focussing on moisture-related vegetation classes for the Teshekpuk Lake region. We associated sediment properties and methane concentrations along a post-drainage succession gradient with remote sensing-derived land cover classes. Our study distinguished five eco-hydrological classes using statistical clustering of vegetation data, which corresponded to the land cover classes. We identified surface wetness and time since drainage as predictors of vegetation composition. Microtopographic complexity increased after drainage. Organic carbon and nitrogen contents in sediment, and dissolved organic carbon (DOC) and dissolved nitrogen (DN) in ponds were high throughout, indicating high organic matter availability and decomposition. We confirmed wetness as a predictor of sediment methane concentrations. Our findings suggest moderate to high methane concentrations independent of drainage age, with particularly high concentrations beneath submerged patches (up to 200μmol l⁻¹) and in pond water (up to 22μmol l⁻¹). In our DLB system, wet and shallow submerged patches with high methane concentrations occupied 54% of the area, and ponds with high DOC, DN and methane occupied another 11%. In conclusion, we demonstrate that DLB wetlands are highly productive regarding organic matter decomposition and methane production. Machine learning-aided land cover classification using high-resolution multispectral satellite imagery provides a useful tool for future upscaling of sediment properties and methane emission potentials from Arctic DLBs.

 

Lakes represent as much as ∼25% of the total land surface area in lowland permafrost regions. Though decreasing lake area has become a widespread phenomenon in permafrost regions, our ability to forecast future patterns of lake drainage spanning gradients of space and time remain limited. Here, we modeled the drivers of gradual (steady declining lake area) and catastrophic (temporally abrupt decrease in lake area) lake drainage using 45 years of Landsat observations (i.e. 1975–2019) across 32 690 lakes spanning climate and environmental gradients across northern Alaska. We mapped lake area using supervised support vector machine classifiers and object based image analyses using five-year Landsat image composites spanning 388 968 km². Drivers of lake drainage were determined with boosted regression tree models, using both static (e.g. lake morphology, proximity to drainage gradient) and dynamic predictor variables (e.g. temperature, precipitation, wildfire). Over the past 45 years, gradual drainage decreased lake area between 10% and 16%, but rates varied over time as the 1990s recorded the highest rates of gradual lake area losses associated with warm periods. Interestingly, the number of catastrophically drained lakes progressively decreased at a rate of ∼37% decade⁻¹from 1975–1979 (102–273 lakes draining year⁻¹) to 2010–2014 (3–8 lakes draining year⁻¹). However this 40 year negative trend was reversed during the most recent time-period (2015–2019), with observations of catastrophic drainage among the highest on record (i.e. 100–250 lakes draining year⁻¹), the majority of which occurred in northwestern Alaska. Gradual drainage processes were driven by lake morphology, summer air and lake temperature, snow cover, active layer depth, and the thermokarst lake settlement index (R²_adj= 0.42, CV = 0.35,p< 0.0001), whereas, catastrophic drainage was driven by the thawing season length, total precipitation, permafrost thickness, and lake temperature (R²_adj= 0.75, CV = 0.67,p< 0.0001). Models forecast a continued decline in lake area across northern Alaska by 15%–21% by 2050. However these estimates are conservative, as the anticipated amplitude of future climate change were well-beyond historical variability and thus insufficient to forecast abrupt ‘catastrophic’ drainage processes. Results highlight the urgency to understand the potential ecological responses and feedbacks linked with ongoing Arctic landscape reorganization.

 

Riverbank erosion in yedoma regions strongly affects landscape evolution, biogeochemical cycling, sediment transport, and organic and nutrient fluxes to the Arctic Ocean. Since 2006, we have studied the 35‐m‐high Itkillik River yedoma bluff in northern Alaska, whose retreat rate during 1995–2010 was up to 19 m/yr, which is among the highest rates worldwide. This study extends our previous observations of bluff evolution and shows that average bluff‐top retreat rates decreased from 8.7–10.0 m/yr during 2011–2014 to 4.5–5.8 m/yr during 2015–2019, and bluff‐base retreat rates for the same time period decreased from 4.7–7.5 m/yr to 1.3–1.7 m/yr, correspondingly. Bluff evolution initially involves rapid fluvio‐thermal erosion at the base and block collapse, following by slowdown in river erosion and continuing thermal denudation of the retreating headwall with formation of baydzherakhs. Eventually, input of sediment and water from the headwall diminishes, vegetation develops, and slope gradually stabilizes. The step change in the fluvial–geomorphic system has resulted in a 60% decline in the volumetric mobilization of sediment and organic carbon between 2011 and 2019. Our findings stress the importance of sustained observations at key permafrost region study sites to elucidate critical information related to past and potential landscape evolution in the Arctic.

 

As the Arctic warms, tundra wildfires are expected to become more frequent and severe. Assessing how the most flammable regions of the tundra respond to burning can inform us about how the rest of the Arctic may be affected by climate change. Here we describe ecosystem responses to tundra fires in the Noatak River watershed of northwestern Alaska using shrub dendrochronology, active‐layer depth monitoring, and remotely sensed vegetation productivity. Results show that relatively productive tundra is more likely to experience fires and to burn more severely, suggesting that fuel loads currently limit tundra fire distribution in the Noatak Valley. Within three years of burning, most alder shrubs sampled had either germinated or resprouted, and vegetation productivity inside 60 burn perimeters had recovered to prefire values. Tundra fires resulted in two phases of increased primary productivity as manifested by increased landscape greening. Phase one occurred in most burned areas 3–10 years after fires, and phase two occurred 16–44 years after fire at sites where tundra fires triggered near‐surface permafrost thaw resulting in shrub proliferation. A fire‐shrub‐greening positive feedback is currently operating in the Noatak Valley and this feedback could expand northward as air temperatures, fire frequencies, and permafrost degradation increase. This feedback will not occur at all locations. In the Noatak Valley, the fire‐shrub‐greening process is relatively limited in tussock tundra communities, where low‐severity fires and shallow active layers exclude shrub proliferation. Climate warming and enhanced fire occurrence will likely shift fire‐poor landscapes into either the tussock tundra or erect‐shrub‐tundra ecological attractor states that now dominate the fire‐rich Noatak Valley.

 

Beavers are starting to colonize low arctic tundra regions in Alaska and Canada, which has implications for surface water changes and ice-rich permafrost degradation. In this study, we assessed the spatial and temporal dynamics of beaver dam building in relation to surface water dynamics and thermokarst landforms using sub-meter resolution satellite imagery acquired between 2002 and 2019 for two tundra areas in northwestern Alaska. In a 100 km²study area near Kotzebue, the number of dams increased markedly from 2 to 98 between 2002 and 2019. In a 430 km²study area encompassing the entire northern Baldwin Peninsula, the number of dams increased from 94 to 409 between 2010 and 2019, indicating a regional trend. Correlating data on beaver dam numbers with surface water area mapped for 12 individual years between 2002 and 2019 for the Kotzebue study area showed a significant positive correlation (R²= 0.61; p < .003). Beaver-influenced waterbodies accounted for two-thirds of the 8.3% increase in total surface water area in the Kotzebue study area during the 17 year period. Beavers specifically targeted thermokarst landforms in their dam building activities. Flooding of drained thermokarst lake basins accounted for 68% of beaver-influenced surface water increases, damming of lake outlets accounted for 26%, and damming of beaded streams accounted for 6%. Surface water increases resulting from beaver dam building likely exacerbated permafrost degradation in the region, but dam failure also factored into the drainage of several thermokarst lakes in the northern Baldwin Peninsula study region, which could promote local permafrost aggradation in freshly exposed lake sediments. Our findings highlight that beaver-driven ecosystem engineering must be carefully considered when accounting for changes occurring in some permafrost regions, and in particular, regional surface water dynamics in low Arctic and Boreal landscapes.

 

### Access Photos of ~50 permaforst boreholes and associated cores can be accessed and downloaded from the 'AR\_Fire\_Core_Photos' directory via: [https://arcticdata.io/data/10.18739/A2251FM9P/](https://arcticdata.io/data/10.18739/A2251FM9P/) ### Overview The Anaktuvuk River tundra fire burned more than 1,000 square kilometers of permafrost-affected arctic tundra in northern Alaska in 2007. The fire is the largest historical recorded tundra fire on the North Slope of Alaska. Fifty percent of the burn area is underlain by Yedoma permafrost that is characterized by extremely high ground-ice content of organic-rich, silty buried soils and the occurrence of large, syngenetic polygonal ice wedges. Given the high ground-ice content of this terrain, Yedoma is thought to be among the most vulnerable to fire-induced thermokarst in the Arctic. With this dataset, we update observations on near-surface permafrost in the Anaktuvuk River tundra fire burn area from 2009 to 2023 using repeat airborne LiDAR-derived elevation data, ground temperature measurements, and cryostratigraphic studies. We have provided all of the data that has gone into an analysis and resulting paper that has been submitted for peer review at the journal Scientific Reports. The datasets include: - 1 m spatial resolution airborne LiDAR-derived digital terrain models from the summers of 2009, 2014, and 2021. - The area in which thaw subsidence was detected in the multi-temporal LiDAR data using the Geomorphic Change Detection software. - A terrain unit map developed for the 50 square kilometer study area. - Ground temperature time series measurements for a logger located in the burned area and a logger located in an unburned area. The ground temperature data consist of daily mean measurements at a depth of 0.15 m (active layer) and 1.00 m (permafrost) from July 2009 to August 2023. - Photos ~50 permafrost boreholes and the associated cores collected there. - A borehole log and notes pdf also accompanies our studies on the cryostratigraphy of permafrost post-fire and our observations on the recovery of permafrost. 

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. 

The University of Alaska Fairbanks T Field is a legacy farm field that is part of the National Science Foundation (NSF) Funded Permafrost Grown project. We are studying the long-term effects of permafrost thaw following initial clearing for cultivation purposes. In this regard, we have acquired very high resolution light detection and ranging (LiDAR) data and digital photography from a DJI M300 drone using a Zenmuse L1 and a MicaSense RedEdge-P camera. The Zenmuse L1 integrates a Livox Lidar module, a high-accuracy inertial measurement units (IMU), and a camera with a 1-inch CMOS on a 3-axis stabilized gimbal. The MicaSense RedEdge-P camera has five multispectral bands and a high-resolution panchromatic band. The drone was configured to fly in real-time kinematic (RTK) mode at an altitude of 60 meters above ground level using the DJI D-RTK 2 base station. Data was acquired using a 50% sidelap and a 70% frontlap for the Zenmuse L1 and an 80% sidelap and a 75% frontlap for the MicaSense. Additional ground control was established with a Leica GS18 global navigation satellite system (GNSS) and all data have been post-processed to World Geodetic System 1984 (WGS84) universal transverse mercator (UTM) Zone 6 North using ellipsoid heights. Data outputs include a two-class-classified LiDAR point cloud, digital surface model, digital terrain model, an orthophoto mosaic, and a multispectral orthoimage consisting of five bands. Image acquisition occurred on 18 August 2023.