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Ice wedges, which are ubiquitous in permafrost areas, play a significant role in the evolution of permafrost landscapes, influencing the topography and hydrology of these regions. In this paper, we combine a detailed multi-generational, interdisciplinary, and international literature review along with our own field experiences to explore the development of low-centered ice-wedge polygons and their orthogonal networks. Low-centered polygons, a type of ice-wedge polygonal ground characterized by elevated rims and lowered wet central basins, are critical indicators of permafrost conditions. The formation of these features has been subject to numerous inconsistencies and debates since their initial description in the 1800s. The development of elevated rims is attributed to different processes, such as soil bulging due to ice-wedge growth, differential frost heave, and the accumulation of vegetation and peat. The transition of low-centered polygons to flat-centered, driven by processes like peat accumulation, aggradational ice formation, and frost heave in polygon centers, has been generally overlooked. Low-centered polygons occur in deltas, on floodplains, and in drained-lake basins. There, they are often arranged in orthogonal networks that comprise a complex system. The prevailing explanation of their formation does not match with several field studies that practically remain unnoticed or ignored. By analyzing controversial subjects, such as the degradational or aggradational nature of low-centered polygons and the formation of orthogonal ice-wedge networks, this paper aims to clarify misconceptions and present a cohesive overview of lowland terrain ice-wedge dynamics. The findings emphasize the critical role of ice wedges in shaping Arctic permafrost landscapes and their vulnerability to ongoing climatic and landscape changes. 

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 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. 

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. 

Abstract. As the northern high latitude permafrost zone experiences accelerated warming, permafrost has become vulnerable to widespread thaw. Simultaneously, wildfire activity across northern boreal forest and Arctic/subarctic tundra regions impact permafrost stability through the combustion of insulating organic matter, vegetation and post-fire changes in albedo. Efforts to synthesise the impacts of wildfire on permafrost are limited and are typically reliant on antecedent pre-fire conditions. To address this, we created the FireALT dataset by soliciting data contributions that included thaw depth measurements, site conditions, and fire event details with paired measurements at environmentally comparable burned and unburned sites. The solicitation resulted in 52,466 thaw depth measurements from 18 contributors across North America and Russia. Because thaw depths were taken at various times throughout the thawing season, we also estimated end of season active layer thickness (ALT) for each measurement using a modified version of the Stefan equation. Here, we describe our methods for collecting and quality checking the data, estimating ALT, the data structure, strengths and limitations, and future research opportunities. The final dataset includes 47,952 ALT estimates (27,747 burned, 20,205 unburned) with 32 attributes. There are 193 unique paired burned/unburned sites spread across 12 ecozones that span Canada, Russia, and the United States. The data span fire events from 1900 to 2022. Time since fire ranges from zero to 114 years. The FireALT dataset addresses a key challenge: the ability to assess impacts of wildfire on ALT when measurements are taken at various times throughout the thaw season depending on the time of field campaigns (typically June through August) by estimating ALT at the end of season maximum. This dataset can be used to address understudied research areas particularly algorithm development, calibration, and validation for evolving process-based models as well as extrapolating across space and time, which could elucidate permafrost-wildfire interactions under accelerated warming across the high northern latitude permafrost zone. The FireALT dataset is available through the Arctic Data Center. 

Abstract. As the northern high-latitude permafrost zone experiences accelerated warming, permafrost has become vulnerable to widespread thaw. Simultaneously, wildfire activity across northern boreal forest and Arctic/subarctic tundra regions impacts permafrost stability through the combustion of insulating organic matter, vegetation, and post-fire changes in albedo. Efforts to synthesis the impacts of wildfire on permafrost are limited and are typically reliant on antecedent pre-fire conditions. To address this, we created the FireALT dataset by soliciting data contributions that included thaw depth measurements, site conditions, and fire event details with paired measurements at environmentally comparable burned and unburned sites. The solicitation resulted in 52 466 thaw depth measurements from 18 contributors across North America and Russia. Because thaw depths were taken at various times throughout the thawing season, we also estimated end-of-season active layer thickness (ALT) for each measurement using a modified version of the Stefan equation. Here, we describe our methods for collecting and quality-checking the data, estimating ALT, the data structure, strengths and limitations, and future research opportunities. The final dataset includes 48 669 ALT estimates with 32 attributes across 9446 plots and 157 burned–unburned pairs spanning Canada, Russia, and the United States. The data span fire events from 1900 to 2022 with measurements collected from 2001 to 2023. The time since fire ranges from 0 to 114 years. The FireALT dataset addresses a key challenge: the ability to assess impacts of wildfire on ALT when measurements are taken at various times throughout the thaw season depending on the time of field campaigns (typically June through August) by estimating ALT at the end-of-season maximum. This dataset can be used to address understudied research areas, particularly algorithm development, calibration, and validation for evolving process-based models as well as extrapolating across space and time, which could elucidate permafrost–wildfire interactions under accelerated warming across the high-northern-latitude permafrost zone. The FireALT dataset is available through the Arctic Data Center (https://doi.org/10.18739/A2RN3092P, Talucci et al., 2024). 

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. 

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. 

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.