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The microtopography associated with ice wedge polygons (IWPs) governs the Arctic ecosystem from local to regional scales due to the impacts on the flow and storage of water and therefore, vegetation and carbon. Increasing subsurface temperatures in Arctic permafrost landscapes cause differential ground settlements followed by a series of adverse microtopographic transitions at sub decadal scale. The entire Arctic has been imaged at 0.5 m or finer resolution by commercial satellite sensors. Dramatic microtopographic transformation of low-centered into high-centered IWPs can be identified using sub-meter resolution commercial satellite imagery. In this exploratory study, we have employed a Deep Learning (DL)-based object detection and semantic segmentation method named the Mask R-CNN to automatically map IWPs from commercial satellite imagery. Different tundra vegetation types have distinct spectral, spatial, textural characteristics, which in turn decide the semantics of overlying IWPs. Landscape complexity translates to the image complexity, affecting DL model performances. Scarcity of labelled training images, inadequate training samples for some types of tundra and class imbalance stand as other key challenges in this study. We implemented image augmentation methods to introduce variety in the training data and trained models separately for tundra types. Augmentation methods show promising results but the models with separate tundra types seem to suffer from the lack of annotated data.

Regional extent and spatiotemporal dynamics of Arctic permafrost disturbances remain poorly quantified. High spatial resolution commercial satellite imagery enables transformational opportunities to observe, map, and document the micro-topographic transitions occurring in Arctic polygonal tundra at multiple spatial and temporal frequencies. The entire Arctic has been imaged at 0.5 m or finer resolution by commercial satellite sensors. The imagery is still largely underutilized, and value-added Arctic science products are rare. Knowledge discovery through artificial intelligence (AI), big imagery, high performance computing (HPC) resources is just starting to be realized in Arctic science. Large-scale deployment of petabyte-scale imagery resources requires sophisticated computational approaches to automated image interpretation coupled with efficient use of HPC resources. In addition to semantic complexities, multitude factors that are inherent to sub-meter resolution satellite imagery, such as file size, dimensions, spectral channels, overlaps, spatial references, and imaging conditions challenge the direct translation of AI-based approaches from computer vision applications. Memory limitations of Graphical Processing Units necessitates the partitioning of an input satellite imagery into manageable sub-arrays, followed by parallel predictions and post-processing to reconstruct the results corresponding to input image dimensions and spatial reference. We have developed a novel high performance image analysis framework –Mapping application for Arctic Permafrost Land Environment (MAPLE) that enables the integration of operational-scale GeoAI capabilities into Arctic science applications. We have designed the MAPLE workflow to become interoperable across HPC architectures while utilizing the optimal use of computing resources.

The utility of sheer volumes of very high spatial resolution (VHSR) commercial imagery in mapping the Arctic region is new and actively evolving. Commercial satellite sensors typically record image data in low-resolution multispectral (MS) and high-resolution panchromatic (PAN) mode. Spatial resolution is needed to accurately describe feature shapes and textural patterns, such as ice-wedge polygons (IWPs) that are rapidly transforming surface features due to degrading permafrost, while spectral resolution allows capturing of land-use and land-cover types. Data fusion, the process of combining PAN and MS images with complementary characteristics often serves as an integral component of remote sensing mapping workflows. The fusion process generates spectral and spatial artifacts that may affect the classification accuracies of subsequent automated image analysis algorithms, such as deep learning (DL) convolutional neural nets (CNN). We employed a detailed multidimensional assessment to understand the performances of an array of eight application-oriented data fusion algorithms when applied to VHSR image scenes for DLCNN-based mapping of ice-wedge polygons. Our findings revealed the scene dependency of data fusion algorithms and emphasized the need for careful selection of the proper algorithm. Results suggested that the fusion algorithms that preserve spatial character of original PAN imagery favor the DLCNN model performances. The choice of fusion approach needs to be considered of equal importance to the required training dataset for successful applications using DLCNN on VHRS imagery in order to enable an accurate mapping effort of permafrost thaw across the Arctic region.

Permafrost thaw has been observed at several locations across the Arctic tundra in recent decades; however, the pan-Arctic extent and spatiotemporal dynamics of thaw remains poorly explained. Thaw-induced differential ground subsidence and dramatic microtopographic transitions, such as transformation of low-centered ice-wedge polygons (IWPs) into high-centered IWPs can be characterized using very high spatial resolution (VHSR) commercial satellite imagery. Arctic researchers demand for an accurate estimate of the distribution of IWPs and their status across the tundra domain. The entire Arctic has been imaged in 0.5 m resolution by commercial satellite sensors; however, mapping efforts are yet limited to small scales and confined to manual or semi-automated methods. Knowledge discovery through artificial intelligence (AI), big imagery, and high performance computing (HPC) resources is just starting to be realized in Arctic science. Large-scale deployment of VHSR imagery resources requires sophisticated computational approaches to automated image interpretation coupled with efficient use of HPC resources. We are in the process of developing an automated Mapping Application for Permafrost Land Environment (MAPLE) by combining big imagery, AI, and HPC resources. The MAPLE uses deep learning (DL) convolutional neural nets (CNNs) algorithms on HPCs to automatically map IWPs from VHSR commercial satellite imagery across large geographic domains. We trained and tasked a DLCNN semantic object instance segmentation algorithm to automatically classify IWPs from VHSR satellite imagery. Overall, our findings demonstrate the robust performances of IWP mapping algorithm in diverse tundra landscapes and lay a firm foundation for its operational-level application in repeated documentation of circumpolar permafrost disturbances.

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season.

Accurate identification of the relationships between permafrost extent and landscape patterns helps develop airborne geophysical or remote sensing tools to map permafrost in remote locations or across large areas. These tools are particularly applicable in discontinuous permafrost where climate warming or disturbances such as human development or fire can lead to rapid permafrost degradation. We linked field-based geophysical, point-scale, and imagery surveying measurements to map permafrost at five fire scars on the Tanana Flats in central Alaska. Ground-based elevation surveys, seasonal thaw-depth profiles, and electrical resistivity tomography (ERT) measurements were combined with airborne imagery and light detection and ranging (LiDAR) to identify relationships between permafrost geomorphology and elapsed time since fire disturbance. ERT was a robust technique for mapping the presence or absence of permafrost because of the marked difference in resistivity values for frozen versus unfrozen material. There was no clear relationship between elapsed time since fire and permafrost extent at our sites. The transition zone boundaries between permafrost soils and unfrozen soils in the collapse-scar bogs at our sites had complex and unpredictable morphologies, suggesting attempts to quantify the presence or absence of permafrost using aerial measurements alone could lead to incomplete results. The results from our study indicated limitations in being able to apply airborne surveying measurements at the landscape scale toward accurately estimating permafrost extent.

Vast mosaics of lakes, wetlands, and rivers on the Arctic Coastal Plain give the impression of water surplus. Yet long winters lock freshwater resources in ice, limiting freshwater habitats and water supply for human uses. Increasingly the petroleum industry relies on lakes to build temporary ice roads for winter oil exploration. Permitting water withdrawal for ice roads in Arctic Alaska is dependent on lake depth, ice thickness, and the fish species present. Recent winter warming suggests that more winter water will be available for ice- road construction, yet high interannual variability in ice thickness and summer precipitation complicates habitat impact assessments. To address these concerns, multidisciplinary researchers are working to understand how Arctic freshwater habitats are responding to changes in both climate and water use in northern Alaska. The dynamics of habitat availability and connectivity are being linked to how food webs support fish and waterbirds across diverse freshwater habitats. Moving toward watershed-scale habitat classification coupled with scenario analysis of climate extremes and water withdrawal is increasingly relevant to future resource management decisions in this region. Such progressive refinement in understanding responses to change provides an example of adaptive management focused on ensuring responsible resource development in the Arctic.

Thermokarst lakes accelerate deep permafrost thaw and the mobilization of previously frozen soil organic carbon. This leads to microbial decomposition and large releases of carbon dioxide (CO2) and methane (CH4) that enhance climate warming. However, the time scale of permafrost-carbon emissions following thaw is not well known but is important for understanding how abrupt permafrost thaw impacts climate feedback. We combined field measurements and radiocarbon dating of CH4 ebullition with (a) an assessment of lake area changes delineated from high-resolution (1–2.5 m) optical imagery and (b) geophysical measurements of thaw bulbs (taliks) to determine the spatiotemporal dynamics of hotspot-seep CH4 ebullition in interior Alaska thermokarst lakes. Hotspot seeps are characterized as point-sources of high ebullition that release 14C-depleted CH4 from deep (up to tens of meters) within lake thaw bulbs year-round. Thermokarst lakes, initiated by a variety of factors, doubled in number and increased 37.5% in area from 1949 to 2009 as climate warmed. Approximately 80% of contemporary CH4 hotspot seeps were associated with this recent thermokarst activity, occurring where 60 years of abrupt thaw took place as a result of new and expanded lake areas. Hotspot occurrence diminished with distance from thermokarst lake margins. We attribute older 14C ages of CH4 released from hotspot seeps in older, expanding thermokarst lakes (14CCH4 20 079 ± 1227 years BP, mean ± standard error (s.e.m.) years) to deeper taliks (thaw bulbs) compared to younger 14CCH4 in new lakes (14CCH4 8526 ± 741 years BP) with shallower taliks. We find that smaller, non-hotspot ebullition seeps have younger 14C ages (expanding lakes 7473 ± 1762 years; new lakes 4742 ± 803 years) and that their emissions span a larger historic range. These observations provide a first-order constraint on the magnitude and decadal-scale duration of CH4-hotspot seep emissions following formation of thermokarst lakes as climate warms.

Climate change is destabilizing permafrost landscapes, affecting infrastructure, ecosystems, and human livelihoods. The rate of permafrost thaw is controlled by surface and subsurface properties and processes, all of which are potentially linked with each other. However, no standardized protocol exists for measuring permafrost thaw and related processes and properties in a linked manner. The permafrost thaw action group of the Terrestrial Multidisciplinary distributed Observatories for the Study of the Arctic Connections (T-MOSAiC) project has developed a protocol, for use by non-specialist scientists and technicians, citizen scientists, and indigenous groups, to collect standardized metadata and data on permafrost thaw. The protocol introduced here addresses the need to jointly measure permafrost thaw and the associated surface and subsurface environmental conditions. The parameters measured along transects include: snow depth, thaw depth, vegetation height, soil texture, and water level. The metadata collection includes data on timing of data collection, geographical coordinates, land surface characteristics (vegetation, ground surface, water conditions), as well as photographs. Our hope is that this openly available dataset will also be highly valuable for validation and parameterization of numerical and conceptual models, and thus to the broad community represented by the T-MOSAiC project.

A slope at Barry Arm, in Alaska’s Prince William Sound, is deforming at a varying rate up to tens of meters per year above a retreating glacier and deep fjord that is a popular recreational destina...

We developed a high-throughput mapping workflow, which centers on deep learning (DL) convolutional neural network (CNN) algorithms on high-performance distributed computing resources, to automatically characterize ice-wedge polygons (IWPs) from sub-meter resolution commercial satellite imagery. We applied a region-based CNN object instance segmentation algorithm, namely the Mask R-CNN, to automatically detect and classify IWPs in North Slope of Alaska. The central goal of our study was to systematically expound the DLCNN model interoperability across varying tundra types (sedge, tussock sedge, and non-tussock sedge) and image scene complexities to refine the understanding of opportunities and challenges for regional-scale mapping applications. We corroborated quantitative error statistics along with detailed visual inspections to gauge the IWP detection accuracies. We found promising model performances (detection accuracies: 89% to 96% and classification accuracies: 94% to 97%) for all candidate image scenes with varying tundra types. The mapping workflow discerned the IWPs by exhibiting low absolute mean relative error (AMRE) values (0.17&ndash, 0.23). Results further suggest the importance of increasing the variability of training samples when practicing transfer-learning strategy to map IWPs across heterogeneous tundra cover types. Overall, our findings demonstrate the robust performances of IWPs mapping workflow in multiple tundra landscapes.

Shrub expansion has been observed across the Arctic in recent decades along with warming air temperatures, but tundra shrub expansion has been most pronounced in protected landscape positions such as floodplains, streambanks, water tracks, and gullies. Here we show through field measurements and laboratory analyses how stream hydrology, permafrost, and soil microbial communities differed between streams in late summer with and without tall shrubs. Our goal was to assess the causes and consequences of tall shrub expansion in Arctic riparian ecosystems. Our results from Toolik Alaska, show greater canopy height and density, and distinctive plant and soil microbial communities along stream sections that lose water into unfrozen ground (talik) compared to gaining sections underlain by shallow permafrost. Leaf Area Index is linearly related to the change in streamflow per unit stream length, with the densest canopies coinciding with increasingly losing stream sections. Considering climate change and the circumpolar scale of riparian shrub expansion, we suggest that permafrost thaw and the resulting talik formation and shift in streamflow regime are occurring across the Low Arctic. Shrub canopy height and density is greater along Arctic streams that lose water into unfrozen ground than along gaining streams, suggest analyses of field measurements from Toolik, Alaska.

The retreat of glaciers in response to global warming has the potential to trigger landslides in glaciated regions around the globe. Landslides that enter fjords or lakes can cause tsunamis, which endanger people and infrastructure far from the landslide itself. Here we document the ongoing movement of an unstable slope (total volume of 455 × 106 m3) in Barry Arm, a fjord in Prince William Sound, Alaska. The slope moved rapidly between 2010 and 2017, yielding a horizontal displacement of 120 m, which is highly correlated with the rapid retreat and thinning of Barry Glacier. Should the entire unstable slope collapse at once, preliminary tsunami modeling suggests a maximum runup of 300 m near the landslide, which may have devastating impacts on local communities. Our findings highlight the need for interdisciplinary studies of recently deglaciated fjords to refine our understanding of the impact of climate change on landslides and tsunamis., Key Points This study quantifies the motion of a large periglacial landslideIt is unique that such a landslide is detected before potential failureThis study calls attention to the glacier‐retreat‐landslide‐tsunami hazards cascade

Although three‐dimensional (3D) seismic surveys have improved the success rate of exploratory drilling for oil and gas, the impacts have received little scientific scrutiny, despite affecting more area than any other oil and gas activity. To aid policy‐makers and scientists, we reviewed studies of the landscape impacts of 3D‐seismic surveys in the Arctic. We analyzed a proposed 3D‐seismic program in northeast Alaska, in the northern Arctic National Wildlife Refuge, which includes a grid 63,000 km of seismic trails and additional camp‐move trails. Current regulations are not adequate to eliminate impacts from these activities. We address issues related to the high‐density of 3D trails compared to 2D methods, with larger crews, more camps, and more vehicles. We focus on consequences to the hilly landscapes, including microtopography, snow, vegetation, hydrology, active layers, and permafrost. Based on studies of 2D‐seismic trails created in 1984–1985 in the same area by similar types of vehicles, under similar regulations, approximately 122 km2 would likely sustain direct medium‐ to high‐level disturbance from the proposed exploration, with possibly expanded impacts through permafrost degradation and hydrological connectivity. Strong winds are common, and snow cover necessary to minimize impacts from vehicles is windblown and inadequate to protect much of the area. Studies of 2D‐seismic impacts have shown that moist vegetation types, which dominate the area, sustain longer‐lasting damage than wet or dry types, and that the heavy vehicles used for mobile camps caused the most damage. The permafrost is ice rich, which combined with the hilly topography, makes it especially susceptible to thermokarst and erosion triggered by winter vehicle traffic. The effects of climate warming will exacerbate the impacts of winter travel due to warmer permafrost and a shift of precipitation from snow to rain. The cumulative impacts of 3D‐seismic traffic in tundra areas need to be better assessed, together with the effects of climate change and the industrial development that would likely follow. Current data needs include studies of the impacts of 3D‐seismic exploration, better climate records for the Arctic National Wildlife Refuge, especially for wind and snow; and high‐resolution maps of topography, ground ice, hydrology, and vegetation.

State-of-the-art deep learning technology has been successfully applied to relatively small selected areas of very high spatial resolution (0.15 and 0.25 m) optical aerial imagery acquired by a fixed-wing aircraft to automatically characterize ice-wedge polygons (IWPs) in the Arctic tundra. However, any mapping of IWPs at regional to continental scales requires images acquired on different sensor platforms (particularly satellite) and a refined understanding of the performance stability of the method across sensor platforms through reliable evaluation assessments. In this study, we examined the transferability of a deep learning Mask Region-Based Convolutional Neural Network (R-CNN) model for mapping IWPs in satellite remote sensing imagery (~0.5 m) covering 272 km2 and unmanned aerial vehicle (UAV) (0.02 m) imagery covering 0.32 km2. Multi-spectral images were obtained from the WorldView-2 satellite sensor and pan-sharpened to ~0.5 m, and a 20 mp CMOS sensor camera onboard a UAV, respectively. The training dataset included 25,489 and 6022 manually delineated IWPs from satellite and fixed-wing aircraft aerial imagery near the Arctic Coastal Plain, northern Alaska. Quantitative assessments showed that individual IWPs were correctly detected at up to 72% and 70%, and delineated at up to 73% and 68% F1 score accuracy levels for satellite and UAV images, respectively. Expert-based qualitative assessments showed that IWPs were correctly detected at good (40–60%) and excellent (80–100%) accuracy levels for satellite and UAV images, respectively, and delineated at excellent (80–100%) level for both images. We found that (1) regardless of spatial resolution and spectral bands, the deep learning Mask R-CNN model effectively mapped IWPs in both remote sensing satellite and UAV images; (2) the model achieved a better accuracy in detection with finer image resolution, such as UAV imagery, yet a better accuracy in delineation with coarser image resolution, such as satellite imagery; (3) increasing the number of training data with different resolutions between the training and actual application imagery does not necessarily result in better performance of the Mask R-CNN in IWPs mapping; (4) and overall, the model underestimates the total number of IWPs particularly in terms of disjoint/incomplete IWPs.

Arctic coastal watersheds, though rarely monitored, are expected to have increased runoff, as climate models predict more precipitation in the Arctic. This study provides a synthesis of streamflow changes in watersheds of the Alaska Arctic Coastal Plain (AACP) based on available historic discharge data and water balance analysis. A comparison of annual runoff from the Putuligayuk River watershed (471 km2) from the period 1970–1986 (78 ± 24.1 mm/yr) to the period 1999–2015 (122 ± 49.6 mm/yr) shows increasing discharge and interannual variability. From this discontinuous record of 32 years, the three lowest runoff years occurred in 1979, 2007, and 2008, and the three highest runoff years occurred in 2003, 2014, and 2015. Other studied AACP watersheds with shorter discharge records demonstrate similar patterns of dry (2007–2008) and wet (2014–2015) years during common periods of observation. A combination of favorable antecedent surface storage conditions and above-average precipitation is required to generate large volumes of surface runoff. A strong relationship between climate, surface storage, and runoff inherent to AACP watersheds makes these systems highly responsive to sea ice retreat and hydrological intensification. Our new estimates of freshwater flux from the AACP to the Beaufort Sea and Chukchi Sea account for an observed range of runoff variability and provide baseline data for modeling arctic hydrologic systems.

Difficulties in obtaining accurate precipitation measurements have limited meaningful hydrologic assessment for over a century due to performance challenges of conventional snowfall and rainfall gauges in windy environments. Here, we compare snowfall observations and bias adjusted snowfall to end‐of‐winter snow accumulation measurements on the ground for 16 years (1999–2014) and assess the implication of precipitation underestimation on the water balance for a low‐gradient tundra wetland near Utqiagvik (formerly Barrow), Alaska (2007–2009). In agreement with other studies, and not accounting for sublimation, conventional snowfall gauges captured 23–56% of end‐of‐winter snow accumulation. Once snowfall and rainfall are bias adjusted, long‐term annual precipitation estimates more than double (from 123 to 274 mm), highlighting the risk of studies using conventional or unadjusted precipitation that dramatically under‐represent water balance components. Applying conventional precipitation information to the water balance analysis produced consistent storage deficits (79 to 152 mm) that were all larger than the largest actual deficit (75 mm), which was observed in the unusually low rainfall summer of 2007. Year‐to‐year variability in adjusted rainfall (±33 mm) was larger than evapotranspiration (±13 mm). Measured interannual variability in partitioning of snow into runoff (29% in 2008 to 68% in 2009) in years with similar end‐of‐winter snow accumulation (180 and 164 mm, respectively) highlights the importance of the previous summer's rainfall (25 and 60 mm, respectively) on spring runoff production. Incorrect representation of precipitation can therefore have major implications for Arctic water budget descriptions that in turn can alter estimates of carbon and energy fluxes., Key Points Water storage deficits are consistently overestimated when precipitation is not adjusted for underestimationEnd‐of‐snowmelt through July rainfall is a more effective measure of drought conditions than total warm season rainfallPrecipitation bias lead to underestimated snowmelt runoff, due to overestimated antecedent water storage deficits and underestimated snowfall

Arctic river discharge has increased in recent decades although sources and mechanisms remain debated. Abundant literature documents permafrost thaw and mountain glacier shrinkage over the past decades. Here we link glacier runoff to aquifer recharge via a losing headwater stream in subarctic Interior Alaska. Field measurements in Jarvis Creek (634 km2), a subbasin of the Tanana and Yukon Rivers, show glacier meltwater runoff as a large component (15–28%) of total annual streamflow despite low glacier cover (3%). About half of annual headwater streamflow is lost to the aquifer (38 to 56%). The estimated long-term change in glacier-derived aquifer recharge exceeds the observed increase in Tanana River base flow. Our findings suggest a linkage between glacier wastage, aquifer recharge along the headwater stream corridor, and lowland winter discharge. Accordingly, glacierized headwater streambeds may serve as major aquifer recharge zones in semiarid climates and therefore contributing to year-round base flow of lowland rivers.

Author(s): Raz-Yaseef, N; Torn, MS; Wu, Y; Billesbach, DP; Liljedahl, AK; Kneafsey, TJ; Romanovsky, VE; Cook, DR; Wullschleger, SD | Abstract: The few prethaw observations of tundra carbon fluxes suggest that there may be large spring releases, but little is known about the scale and underlying mechanisms of this phenomenon. To address these questions, we combined ecosystem eddy flux measurements from two towers near Barrow, Alaska, with mechanistic soil-core thawing experiment. During a 2 week period prior to snowmelt in 2014, large fluxes were measured, reducing net summer uptake of CO2 by 46% and adding 6% to cumulative CH4 emissions. Emission pulses were linked to unique rain-on-snow events enhancing soil cracking. Controlled laboratory experiment revealed that as surface ice thaws, an immediate, large pulse of trapped gases is emitted. These results suggest that the Arctic CO2 and CH4 spring pulse is a delayed release of biogenic gas production from the previous fall and that the pulse can be large enough to offset a significant fraction of the moderate Arctic tundra carbon sink.

Very high spatial resolution commercial satellite imagery can inform observation, mapping, and documentation of micro-topographic transitions across large tundra regions. The bridging of fine-scale field studies with pan-Arctic system assessments has until now been constrained by a lack of overlap in spatial resolution and geographical coverage. This likely introduced biases in climate impacts on, and feedback from the Arctic region to the global climate system. The central objective of this exploratory study is to develop an object-based image analysis workflow to automatically extract ice-wedge polygon troughs from very high spatial resolution commercial satellite imagery. We employed a systematic experiment to understand the degree of interoperability of knowledge-based workflows across distinct tundra vegetation units—sedge tundra and tussock tundra—focusing on the same semantic class. In our multi-scale trough modelling workflow, we coupled mathematical morphological filtering with a segmentation process to enhance the quality of image object candidates and classification accuracies. Employment of the master ruleset on sedge tundra reported classification accuracies of correctness of 0.99, completeness of 0.87, and F1 score of 0.92. When the master ruleset was applied to tussock tundra without any adaptations, classification accuracies remained promising while reporting correctness of 0.87, completeness of 0.77, and an F1 score of 0.81. Overall, results suggest that the object-based image analysis-based trough modelling workflow exhibits substantial interoperability across the terrain while producing promising classification accuracies. From an Arctic earth science perspective, the mapped troughs combined with the ArcticDEM can allow hydrological assessments of lateral connectivity of the rapidly changing Arctic tundra landscape, and repeated mapping can allow us to track fine-scale changes across large regions and that has potentially major implications on larger riverine systems.

Accurate identification of the relationships between permafrost extent and landscape patterns can help to develop airborne geophysical or remote sensing tools to map permafrost in remote locations or across large areas. These tools will be particularly applicable in discontinuous permafrost where climate warming or disturbances such as human development or fire can lead to rapid permafrost degradation. We have linked field-based geophysical, point-scale, and imagery surveying measurements to map permafrost at five fire scars (1930, 1975, 1988, 2001, and 2010) on the Tanana Flats in central Alaska. Ground-based elevation surveys, seasonal thaw-depth profiles, and electrical resistivity tomography (ERT) measurements were combined with airborne imagery and light detection and ranging (LiDAR) to identify relationships between permafrost geomorphology and elapsed time since fire disturbance. ERT proved to be a robust technique for mapping the presence or absence of permafrost because of the marked difference in resistivity values for frozen versus unfrozen material. There was no clear relationship between elapsed time since fire and permafrost extent at our sites. However, we have found that the transition zone boundaries between permafrost soils and unfrozen soils in the collapse-scar bogs at our sites had complex and unpredictable morphologies. This result suggested that attempts to quantify the presence or absence of permafrost using aerial measurements alone could lead to incomplete results. Taken in total, the results from our study indicated that although ground-based ERT measurements were the most rapid means of mapping permafrost, we were still limited in being able to apply airborne surveying measurements at the landscape scale toward accurately estimating permafrost extent.

Lakes are prevalent in the Arctic and thus play a key role in regional hydrology. Since many Arctic lakes are shallow and ice grows thick (historically 2 m or greater), seasonal ice commonly freezes to the lake bed (bedfast ice) by winter's end. Bedfast ice fundamentally alters lake energy balance and melt-out processes compared to deeper lakes that exceed the maximum ice thickness (floating ice) and maintain perennial liquid water below floating ice. Our analysis of lakes in northern Alaska indicated that ice-out of bedfast ice lakes occurred on average 17 days earlier (22 June) than ice-out on adjacent floating ice lakes (9 July). Earlier ice-free conditions in bedfast ice lakes caused higher open-water evaporation, 28% on average, relative to floating ice lakes and this divergence increased in lakes closer to the coast and in cooler summers. Water isotopes (18O and 2H) indicated similar differences in evaporation between these lake types. Our analysis suggests that ice regimes created by the combination of lake depth relative to ice thickness and associated ice-out timing currently cause a strong hydrologic divergence among Arctic lakes. Thus, understanding the distribution and dynamics of lakes by ice regime is essential for predicting regional hydrology. An observed regime shift in lakes to floating ice conditions due to thinner ice growth may initially offset lake drying because of lower evaporative loss from this lake type. This potential negative feedback caused by winter processes occurs in spite of an overall projected increase in evapotranspiration as the Arctic climate warms.

This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using ground-penetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS). We found that GPR data provided high-precision estimates of snow depth (RMSE = 2.9 cm), with a spatial sampling of 10 cm along transects. Phodar-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing, while yielding a high precision (RMSE = 6.0 cm) and a fine spatial sampling (4 cm × 4 cm). We then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the snow-free lidar digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free lidar DEM and multiscale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSE = 6.0 cm), at 0.5 m resolution and over the lidar domain (750 m × 700 m).

Climatic changes are most pronounced in northern high latitude regions. Yet, there is a paucity of observational data, both spatially and temporally, such that regional-scale dynamics are not fully captured, limiting our ability to make reliable projections. In this study, a group of dynamical downscaling products were created for the period 1950 to 2100 to better understand climate change and its impacts on hydrology, permafrost, and ecosystems at a resolution suitable for northern Alaska. An ERA-interim reanalysis dataset and the Community Earth System Model (CESM) served as the forcing mechanisms in this dynamical downscaling framework, and the Weather Research & Forecast (WRF) model, embedded with an optimization for the Arctic (Polar WRF), served as the Regional Climate Model (RCM). This downscaled output consists of multiple climatic variables (precipitation, temperature, wind speed, dew point temperature, and surface air pressure) for a 10 km grid spacing at three-hour intervals. The modeling products were evaluated and calibrated using a bias-correction approach. The ERA-interim forced WRF (ERA-WRF) produced reasonable climatic variables as a result, yielding a more closely correlated temperature field than precipitation field when long-term monthly climatology was compared with its forcing and observational data. A linear scaling method then further corrected the bias, based on ERA-interim monthly climatology, and bias-corrected ERA-WRF fields were applied as a reference for calibration of both the historical and the projected CESM forced WRF (CESM-WRF) products. Biases, such as, a cold temperature bias during summer and a warm temperature bias during winter as well as a wet bias for annual precipitation that CESM holds over northern Alaska persisted in CESM-WRF runs. The linear scaling of CESM-WRF eventually produced high-resolution downscaling products for the Alaskan North Slope for hydrological and ecological research, together with the calibrated ERA-WRF run, and its capability extends far beyond that. Other climatic research has been proposed, including exploration of historical and projected climatic extreme events and their possible connections to low-frequency sea-atmospheric oscillations, as well as near-surface permafrost degradation and ice regime shifts of lakes. These dynamically downscaled, bias corrected climatic datasets provide improved spatial and temporal resolution data necessary for ongoing modeling efforts in northern Alaska focused on reconstructing and projecting hydrologic changes, ecosystem processes and responses, and permafrost thermal regimes. The dynamical downscaling methods presented in this study can also be used to create more suitable model input datasets for other sub-regions of the Arctic. Supplementary data are available at https://doi.org/10.1594/PANGAEA.863625.

The microtopography associated with ice-wedge polygons governs many aspects of Arctic ecosystem, permafrost, and hydrologic dynamics from local to regional scales owing to the linkages between microtopography and the flow and storage of water, vegetation succession, and permafrost dynamics. Wide-spread ice-wedge degradation is transforming low-centered polygons into high-centered polygons at an alarming rate. Accurate data on spatial distribution of ice-wedge polygons at a pan-Arctic scale are not yet available, despite the availability of sub-meter-scale remote sensing imagery. This is because the necessary spatial detail quickly produces data volumes that hamper both manual and semi-automated mapping approaches across large geographical extents. Accordingly, transforming big imagery into ‘science-ready’ insightful analytics demands novel image-to-assessment pipelines that are fueled by advanced machine learning techniques and high-performance computational resources. In this exploratory study, we tasked a deep-learning driven object instance segmentation method (i.e., the Mask R-CNN) with delineating and classifying ice-wedge polygons in very high spatial resolution aerial orthoimagery. We conducted a systematic experiment to gauge the performances and interoperability of the Mask R-CNN across spatial resolutions (0.15 m to 1 m) and image scene contents (a total of 134 km2) near Nuiqsut, Northern Alaska. The trained Mask R-CNN reported mean average precisions of 0.70 and 0.60 at thresholds of 0.50 and 0.75, respectively. Manual validations showed that approximately 95% of individual ice-wedge polygons were correctly delineated and classified, with an overall classification accuracy of 79%. Our findings show that the Mask R-CNN is a robust method to automatically identify ice-wedge polygons from fine-resolution optical imagery. Overall, this automated imagery-enabled intense mapping approach can provide a foundational framework that may propel future pan-Arctic studies of permafrost thaw, tundra landscape evolution, and the role of high latitudes in the global climate system.