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Snowdrifts formed by wind transported snow deposition represent a vital component of the earth surface processes on Arctic tundra. Snow accumulation on steep slopes particularly at the margins of rivers, coasts, lakes, and drained lake basins (DLBs) comprise a significant water storage component for the ecosystem during spring and summer snowmelt. The tundra landscape is in constant change as lakes drain, substantially altering the surface morphology that partially controls how snow drifts and accumulates throughout the cold seasons. Here, we combine field measurements, remote sensing observations, and snow modeling to investigate how lake drainage affects snow redistribution at Inigok on the Arctic Coastal Plain of Alaska, where the snow movement is controlled by wind. Field observations included measurements of snow depth using ground penetrating radar and probe. We mapped mid‐July snow cover and modeled snow redistribution before and after drainage simulation for 33 lakes (∼30 km²) in our study area (∼140 km²). Our results show the advantage of using a wide range of snow depth measurements on frozen lakes, DLBs, and upland to validate the snow modeling in order to capture the variability inherent in the landscape. The lake drainage simulation suggests an increase in snow storage of up to ∼24% at DLBs compared to extant lakes, ∼35% considering only snowdrifts (assumed as ≥1 m depth), and ∼4% considering the whole study area. This increase in snow accumulation could significantly impact the landscape when it melts, including wildlife, vegetation, biogeochemical processes, and potential natural hazards like snow‐dam outburst floods.

 

The accelerated warming conditions of the high Arctic have intensified the extensive thawing of permafrost. Retrogressive thaw slumps (RTSs) are considered as the most active landforms in the Arctic permafrost. An increase in RTSs has been observed in the Arctic in recent decades. Continuous monitoring of RTSs is important to understand climate change-driven disturbances in the region. Manual detection of these landforms is extremely difficult as they occur over exceptionally large areas. Only very few studies have explored the utility of very high spatial resolution (VHSR) commercial satellite imagery in the automated mapping of RTSs. We have developed deep learning (DL) convolution neural net (CNN) based workflow to automatically detect RTSs from VHRS satellite imagery. This study systematically compared the performance of different DLCNN model architectures and varying backbones. Our candidate CNN models include: DeepLabV3+, UNet, UNet++, Multi-scale Attention Net (MA-Net), and Pyramid Attention Network (PAN) with ResNet50, ResNet101 and ResNet152 backbones. The RTS modeling experiment was conducted on Banks Island and Ellesmere Island in Canada. The UNet++ model demonstrated the highest accuracy (F1 score of 87%) with the ResNet50 backbone at the expense of training and inferencing time. PAN, DeepLabV3, MaNet, and UNet, models reported mediocre F1 scores of 72%, 75%, 80%, and 81% respectively. Our findings unravel the performances of different DLCNNs in imagery-enabled RTS mapping and provide useful insights on operationalizing the mapping application across the Arctic.