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Few fires are known to have burned the tundra of the Arctic Slope north of the Brooks Range in Alaska, USA. A total of 90 fires between 1969 and 2022 are known. Because fire has been rare, old burns can be detected by the traces of thermokarst and distinct vegetation they leave in otherwise uniform tundra, which are visible in aerial photograph archives. Several prehistoric tundra burns have been found in this way. Detection of tundra fires in this sparsely populated and remote area has been historically inconsistent and opportunistic, relying on reports by aircraft pilots. Fire reports have been logged into an administrative database which, out of necessity, has been used to scientifically evaluate changes in the fire regime. To improve the consistency of the record, we completed a systematic search of Landsat Collection 2 for the Brooks Range Foothills ecoregion over the period 1972–2022. We found 57 unrecorded tundra burns, about 41% of the total, which now numbers 138. Only 15% and 33% of all fires appear in MODIS and VIIRS satellite-borne thermal anomaly products, respectively. The fire frequency in the first 37 years of the record is 0.89 y−1 for natural ignitions that spread ≥10 ha. Frequencymore »in the last 13 years is 2.5 y−1, indicating a nearly three-fold increase in fire frequency.« less

Abstract Beavers were not previously recognized as an Arctic species, and their engineering in the tundra is considered negligible. Recent findings suggest that beavers have moved into Arctic tundra regions and are controlling surface water dynamics, which strongly influence permafrost and landscape stability. Here we use 70 years of satellite images and aerial photography to show the scale and magnitude of northwestward beaver expansion in Alaska, indicated by the construction of over 10,000 beaver ponds in the Arctic tundra. The number of beaver ponds doubled in most areas between ~ 2003 and ~ 2017. Earlier stages of beaver engineering are evident in ~ 1980 imagery, and there is no evidence of beaver engineering in ~ 1952 imagery, consistent with observations from Indigenous communities describing the influx of beavers over the period. Rapidly expanding beaver engineering has created a tundra disturbance regime that appears to be thawing permafrost and exacerbating the effects of climate change.

Retrogressive thaw slumps (RTS) are considered one of the most dynamic permafrost disturbance features in the Arctic. Sub-meter resolution multispectral imagery acquired by very high spatial resolution (VHSR) commercial satellite sensors offer unique capacities in capturing the morphological dynamics of RTSs. The central goal of this study is to develop a deep learning convolutional neural net (CNN) model (a UNet-based workflow) to automatically detect and characterize RTSs from VHSR imagery. We aimed to understand: (1) the optimal combination of input image tile size (array size) and the CNN network input size (resizing factor/spatial resolution) and (2) the interoperability of the trained UNet models across heterogeneous study sites based on a limited set of training samples. Hand annotation of RTS samples, CNN model training and testing, and interoperability analyses were based on two study areas from high-Arctic Canada: (1) Banks Island and (2) Axel Heiberg Island and Ellesmere Island. Our experimental results revealed the potential impact of image tile size and the resizing factor on the detection accuracies of the UNet model. The results from the model transferability analysis elucidate the effects on the UNet model due the variability (e.g., shape, color, and texture) associated with the RTS training samples. Overall,more »study findings highlight several key factors that we should consider when operationalizing CNN-based RTS mapping over large geographical extents.« less

Remote sensing-based Earth Observation plays an important role in assessing environmental changes throughout our planet. As an image-heavy domain, the evaluation of the data strongly focuses on statistical and pixel-based spatial analysis methods. However, considering the complexity of our Earth system, there are some environmental structures and dependencies that are not possible to accurately describe with these traditional image analysis approaches. One example for such a limitation is the representation of (spatial) networks and their characteristics. In this study, we thus propose a computer vision approach that enables the representation of semantic information gained from images as graphs. As an example, we investigate digital terrain models of Arctic permafrost landscapes with its very characteristic polygonal patterned ground. These regular patterns, which are clearly visible in high-resolution image and elevation data, are formed by subsurface ice bodies that are very vulnerable to rising temperatures in a warming Arctic. Observing these networks’ topologies and metrics in space and time with graph analysis thus allows insights into the landscape’s complex geomorphology, hydrology, and ecology and therefore helps to quantify how they interact with climate change. We show that results extracted with this analytical and highly automated approach are in line with those gatheredmore »from other manual studies or from manual validation. Thus, with this approach, we introduce a method that, for the first time, enables upscaling of such terrain and network analysis to potentially pan-Arctic scales where collecting in-situ field data is strongly limited.« less

Environmental impact assessments for new Arctic infrastructure do not adequately consider the likely long-term cumulative effects of climate change and infrastructure to landforms and vegetation in areas with ice-rich permafrost, due in part to lack of long-term environmental studies that monitor changes after the infrastructure is built. This case study examines long-term (1949–2020) climate- and road-related changes in a network of ice-wedge polygons, Prudhoe Bay Oilfield, Alaska. We studied four trajectories of change along a heavily traveled road and a relatively remote site. During 20 years prior to the oilfield development, the climate and landscapes changed very little. During 50 years after development, climate-related changes included increased numbers of thermokarst ponds, changes to ice-wedge-polygon morphology, snow distribution, thaw depths, dominant vegetation types, and shrub abundance. Road dust strongly affected plant-community structure and composition, particularly small forbs, mosses, and lichens. Flooding increased permafrost degradation, polygon center-trough elevation contrasts, and vegetation productivity. It was not possible to isolate infrastructure impacts from climate impacts, but the combined datasets provide unique insights into the rate and extent of ecological disturbances associated with infrastructure-affected landscapes under decades of climate warming. We conclude with recommendations for future cumulative impact assessments in areas with ice-rich permafrost.

Abstract. Lakes in the Arctic are important reservoirs of heat withmuch lower albedo in summer and greater absorption of solar radiation thansurrounding tundra vegetation. In the winter, lakes that do not freeze totheir bed have a mean annual bed temperature >0 ∘C inan otherwise frozen landscape. Under climate warming scenarios, we expectArctic lakes to accelerate thawing of underlying permafrost due to warmingwater temperatures in the summer and winter. Previous studies of Arcticlakes have focused on ice cover and thickness, the ice decay process,catchment hydrology, lake water balance, and eddy covariance measurements,but little work has been done in the Arctic to model lake heat balance. Weapplied the LAKE 2.0 model to simulate water temperatures in three Arcticlakes in northern Alaska over several years and tested the sensitivity ofthe model to several perturbations of input meteorological variables(precipitation, shortwave radiation, and air temperature) and several modelparameters (water vertical resolution, sediment vertical resolution, depthof soil column, and temporal resolution). The LAKE 2.0 model is aone-dimensional model that explicitly solves vertical profiles of waterstate variables on a grid. We used a combination of meteorological data fromlocal and remote weather stations, as well as data derived from remotesensing, to drive the model. We validated modeled water temperatures withdatamore »of observed lake water temperatures at several depths over severalyears for each lake. Our validation of the LAKE 2.0 model is a necessarystep toward modeling changes in Arctic lake ice regimes, lake heat balance,and thermal interactions with permafrost. The sensitivity analysis shows usthat lake water temperature is not highly sensitive to small changes in airtemperature or precipitation, while changes in shortwave radiation and largechanges in precipitation produced larger effects. Snow depth and lake icestrongly affect water temperatures during the frozen season, which dominatesthe annual thermal regime of Arctic lakes. These findings suggest thatreductions in lake ice thickness and duration could lead to more heatstorage by lakes and enhanced permafrost degradation.« less

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 themore »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.« less

Abstract. Sea level rise and coastal erosion have inundated large areas of Arctic permafrost. Submergence by warm and saline waters increases the rate of inundated permafrost thaw compared to sub-aerial thawing on land. Studying the contact between the unfrozen and frozen sediments below the seabed, also known as the ice-bearing permafrost table (IBPT), provides valuable information to understand the evolution of sub-aquatic permafrost, which is key to improving and understanding coastal erosion prediction models and potential greenhouse gas emissions. In this study, we use data from 2D electrical resistivity tomography (ERT) collected in the nearshore coastal zone of two Arctic regions that differ in their environmental conditions (e.g., seawater depth and resistivity) to image and study the subsea permafrost. The inversion of 2D ERT data sets is commonly performed using deterministic approaches that favor smoothed solutions, which are typically interpreted using a user-specified resistivity threshold to identify the IBPT position. In contrast, to target the IBPT position directly during inversion, we use a layer-based model parameterization and a global optimization approach to invert our ERT data. This approach results in ensembles of layered 2D model solutions, which we use to identify the IBPT and estimate the resistivity of the unfrozenmore »and frozen sediments, including estimates of uncertainties. Additionally, we globally invert 1D synthetic resistivity data and perform sensitivity analyses to study, in a simpler way, the correlations and influences of our model parameters. The set of methods provided in this study may help to further exploit ERT data collected in such permafrost environments as well as for the design of future field experiments.« less