Search for: All records

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.

 

This dataset contains information on cryostratigraphy and ground-ice content of the upper permafrost, which was based on the results of 22 field trips in 2018-2023. Field studies were performed in various regions of Alaska and Canadian Arctic including the following study areas: Utqiagvik (former Barrow), Teshekpuk Lake, Prudhoe Bay Oilfield, Toolik Lake, Jago River, Itkillik River, Anaktuvuk River, Fairbanks, Dalton Highway, Glennallen, Point Lay, Bylot Island (Canada), Inuvik-Tuktoyaktuk (Canada). Cryostratigraphy of the upper permafrost was studied mainly in coastal and riverbank exposures and frozen cores obtained from drilling with the SIPRE corer. Permafrost exposures and cores were described and photographed in the field, and obtained soil samples were delivered to the University of Alaska Fairbanks for additional descriptions and analyses. Ice contents of frozen soils (including gravimetric and volumetric moisture content, excess-ice content) were measured. The dataset includes cryostratigraphic descriptions, gravimetric (GMC) and volumetric (VMC) moisture content, excess-ice content (EIC), electrical conductivity (EC) and photographs of the permafrost exposures and frozen cores obtained from boreholes. 

Abstract The permafrost–fire–climate system has been a hotspot in research for decades under a warming climate scenario. Surface vegetation plays a dominant role in protecting permafrost from summer warmth, thus, any alteration of vegetation structure, particularly following severe wildfires, can cause dramatic top–down thaw. A challenge in understanding this is to quantify fire-induced thaw settlement at large scales (>1000 km 2 ). In this study, we explored the potential of using Landsat products for a large-scale estimation of fire-induced thaw settlement across a well-studied area representative of ice-rich lowland permafrost in interior Alaska. Six large fires have affected ∼1250 km 2 of the area since 2000. We first identified the linkage of fires, burn severity, and land cover response, and then developed an object-based machine learning ensemble approach to estimate fire-induced thaw settlement by relating airborne repeat lidar data to Landsat products. The model delineated thaw settlement patterns across the six fire scars and explained ∼65% of the variance in lidar-detected elevation change. Our results indicate a combined application of airborne repeat lidar and Landsat products is a valuable tool for large scale quantification of fire-induced thaw settlement. 

Data are available for download at http://arcticdata.io/data/10.18739/A2KW57K57 Permafrost can be indirectly detected via remote sensing techniques through the presence of ice-wedge polygons, which are a ubiquitous ground surface feature in tundra regions. Ice-wedge polygons form through repeated annual cracking of the ground during cold winter days. In spring, the cracks fill in with snowmelt water, creating ice wedges, which are connected across the landscape in an underground network and that can grow to several meters depth and width. The growing ice wedges push the soil upwards, forming ridges that bound low-centered ice-wedge polygons. If the top of the ice wedge melts, the ground subsides and the ridges become troughs and the ice-wedge polygons become high-centered. Here, a Convolutional Neural Network is used to map the boundaries of individual ice-wedge polygons based on high-resolution commercial satellite imagery obtained from the Polar Geospatial Center. This satellite imagery used for the detection of ice-wedge polygons represent years between 2001 and 2021, so this dataset represents ice-wedge polygons mapped from different years. This dataset does not include a time series (i.e. same area mapped more than once). The shapefiles are masked, reprojected, and processed into GeoPackages with calculated attributes for each ice-wedge polygon such as circumference and width. The GeoPackages are then rasterized with new calculated attributes for ice-wedge polygon coverage such a coverage density. This release represents the region classified as “high ice” by Brown et al. 1997. The dataset is available to explore on the Permafrost Discovery Gateway (PDG), an online platform that aims to make big geospatial permafrost data accessible to enable knowledge-generation by researchers and the public. The PDG project creates various pan-Arctic data products down to the sub-meter and monthly resolution. Access the PDG Imagery Viewer here: https://arcticdata.io/catalog/portals/permafrost Data limitations in use: This data is part of an initial release of the pan-Arctic data product for ice-wedge polygons, and it is expected that there are constraints on its accuracy and completeness. Users are encouraged to provide feedback regarding how they use this data and issues they encounter during post-processing. Please reach out to the dataset contact or a member of the PDG team via support@arcticdata.io. 

Understanding the key mechanisms that control northern treelines is important to accurately predict biome shifts and terrestrial feedbacks to climate. At a global scale, it has long been observed that elevational and latitudinal treelines occur at similar mean growing season air temperature (GSAT) isotherms, inspiring the growth limitation hypothesis (GLH) that cold GSAT limits aboveground growth of treeline trees, with mean treeline GSAT ~6-7 degrees celsius (°C). Treelines with mean GSAT warmer than 6-7 °C may indicate other limiting factors. Many treelines globally are not advancing despite warming, and other climate variables are rarely considered at broad scales. Our goals were to test whether current boreal treelines in northern Alaska correspond with the GLH isotherm, determine which environmental factors are most predictive of treeline presence, and to identify areas beyond the current treeline where advance is most likely. We digitized ~12,400 kilometers (km) of treelines (greater than 26K (26,000) points) and computed seasonal climate variables across northern Alaska. We then built a generalized additive model predicting treeline presence to identify key factors determining treeline. Two metrics of mean GSAT at Alaska’s northern treelines were consistently warmer than the 6-7 °C isotherm (means of 8.5 °C and 9.3 °C), indicating that direct physiological limitation from low GSAT is unlikely to explain the position of treelines in northern Alaska. Our final model included cumulative growing degree-days, near-surface (≤ 1 meters (m)) permafrost probability, and growing season total precipitation, which together may represent the importance of soil temperature. Our results indicate that mean GSAT may not be the primary driver of treeline in northern Alaska or that its effect is mediated by other more proximate, and possibly non-climatic, controls. Our model predicts treeline potential in several areas beyond current treelines, pointing to possible routes of treeline advance if unconstrained by non-climatic factors. 

Understanding the key mechanisms that control northern treelines is important to accurately predict biome shifts and terrestrial feedbacks to climate. At a global scale, it has long been observed that elevational and latitudinal treelines occur at similar mean growing season air temperature (GSAT) isotherms, inspiring the growth limitation hypothesis (GLH) that cold GSAT limits aboveground growth of treeline trees, with mean treeline GSAT ~6-7 degrees celsius (°C). Treelines with mean GSAT warmer than 6-7 °C may indicate other limiting factors. Many treelines globally are not advancing despite warming, and other climate variables are rarely considered at broad scales. Our goals were to test whether current boreal treelines in northern Alaska correspond with the GLH isotherm, determine which environmental factors are most predictive of treeline presence, and to identify areas beyond the current treeline where advance is most likely. We digitized ~12,400 kilometers (km) of treelines (greater than 26K points) and computed seasonal climate variables across northern Alaska. We then built a generalized additive model predicting treeline presence to identify key factors determining treeline. Two metrics of mean GSAT at Alaska’s northern treelines were consistently warmer than the 6-7 °C isotherm (means of 8.5 °C and 9.3 °C), indicating that direct physiological limitation from low GSAT is unlikely to explain the position of treelines in northern Alaska. Our final model included cumulative growing degree-days, near-surface (≤ 1 meters (m)) permafrost probability, and growing season total precipitation, which together may represent the importance of soil temperature. Our results indicate that mean GSAT may not be the primary driver of treeline in northern Alaska or that its effect is mediated by other more proximate, and possibly non-climatic, controls. Our model predicts treeline potential in several areas beyond current treelines, pointing to possible routes of treeline advance if unconstrained by non-climatic factors. 

Abstract Alaska has diverse boreal ecosystems across heterogeneous landscapes driven by a wide range of biological and geomorphic processes associated with disturbance and successional patterns under a changing climate. To assess historical patterns and rates of change, we quantified the areal extent of ecotypes and the biophysical factors driving change through photo-interpretation of 2200 points on a time-series (∼1949, ∼1978, ∼2007, ∼2017) of geo-rectified imagery for 22 grids across central Alaska. Overall, 68.6% of the area had changes in ecotypes over ∼68 years. Most of the change resulted from increases in upland and lowland forest types, with an accompanying decrease in upland and lowland scrub types, as post-fire succession led to mid- and late-successional stages. Of 17 drivers of landscape change, fire was by far the largest, affecting 46.5% of the region overall from 1949 to 2017. Fire was notably more extensive in the early 1900s. Thermokarst nearly doubled from 3.9% in 1949 to 6.3% in 2017. Riverine ecotypes covered 7.8% area and showed dynamic changes related to channel migration and succession. Using past rates of ecotype transitions, we developed four state-transition models to project future ecotype extent based on historical rates, increasing temperatures, and driver associations. Ecotype changes from 2017 to 2100, nearly tripled for the driver-adjusted RCP6.0 temperature model (30.6%) compared to the historical rate model (11.5%), and the RCP4.5 (12.4%) and RCP8.0 (14.7%) temperature models. The historical-rate model projected 38 ecotypes will gain area and 24 will lose area by 2100. Overall, disturbance and recovery associated with a wide range of drivers across the patchy mosaic of differing aged ecotypes led to a fairly stable overall composition of most ecotypes over long intervals, although fire caused large temporal fluctuations for many ecotypes. Thermokarst, however, is accelerating and projected to have increasingly transformative effects on future ecotype distributions. 

Recent excavation in the new CRREL Permafrost Tunnel in Fox, Alaska provides a unique opportunity to study properties of Yedoma — late Pleistocene ice- and organic-rich syngenetic permafrost. Yedoma has been described at numerous sites across Interior Alaska, mainly within the Yukon-Tanana upland. The most comprehensive data on the structure and properties of Yedoma in this area have been obtained in the CRREL Permafrost Tunnel near Fairbanks — one of the most accessible large-scale exposures of Yedoma permafrost on Earth, which became available to researchers in the mid-1960s. Expansion of the new ∼4-m-high and ∼4-m-wide linear excavations, started in 2011 and ongoing, exposes an additional 300 m of well-preserved Yedoma and provides access to sediments deposited over the past 40,000 years, which will allow us to quantify rates and patterns of formation of syngenetic permafrost, depositional history and biogeochemical characteristics of Yedoma, and its response to a warmer climate. In this paper, we present results of detailed cryostratigraphic studies in the Tunnel and adjacent area. Data from our study include ground-ice content, the stable water isotope composition of the variety of ground-ice bodies, and radiocarbon age dates. Based on cryostratigraphic mapping of the Tunnel and results of drilling above and inside the Tunnel, six main cryostratigraphic units have been distinguished: 1) active layer; 2) modern intermediate layer (ice-rich silt); 3) relatively ice-poor Yedoma silt reworked by thermal erosion and thermokarst during the Holocene; 4) ice-rich late Pleistocene Yedoma silt with large ice wedges; 5) relatively ice-poor fluvial gravel; and 6) ice-poor bedrock. Our studies reveal significant differences in cryostratigraphy of the new and old CRREL Permafrost Tunnel facilities. Original syngenetic permafrost in the new Tunnel has been better preserved and less affected by erosional events during the period of Yedoma formation, although numerous features (e.g., bodies of thermokarst-cave ice, thaw unconformities, buried gullies) indicate the original Yedoma silt in the recently excavated sections was also reworked to some extent by thermokarst and thermal erosion during the late Pleistocene and Holocene. 

Since the discovery of frozen megafauna carcasses in Northern Siberia and Alaska in the early 1800s, the Yedoma phenomenon has attracted many Arctic explorers and scientists. Exposed along coastal and riverbank bluffs, Yedoma often appears as large masses of ice with some inclusions of sediment. The ground ice particularly mystified geologists and geographers, and they considered sediment within Yedoma exposures to be a secondary and unimportant component. Numerous scientists around the world tried to explain the origin of Yedoma for decades, even though some of them had never seen Yedoma in the field. The origin of massive ice in Yedoma has been attributed to buried surface ice (glaciers, snow, lake ice, and icings), intrusive ice (open system pingo), and finally to ice wedges. Proponents of the last hypothesis found it difficult to explain a vertical extent of ice wedges, which in some cases exceeds 40 m. It took over 150 years of intense debates to understand the process of ice-wedge formation occurring simultaneously (syngenetically) with soil deposition and permafrost aggregation. This understanding was based on observations of the contemporary formation of syngenetic permafrost with ice wedges on the floodplains of Arctic rivers. It initially was concluded that Yedoma was a floodplain deposit, and it took several decades of debates to understand that Yedoma is of polygenetic origin. In this paper, we discuss the history of Yedoma studies from the early 19th century until the 1980s—the period when the main hypotheses of Yedoma origin were debated and developed.