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The paper is concerned with thermal disturbances in continuous permafrost due to open pit mining and tailings impoundment in the cold regions of Northern Canada. Numerical simulations were conducted to investigate issues of thermal regime changes and permafrost degradation in both the short term and the long term in connection with the proposed Kiggavik project in Nunavut, Canada. The results of numerical simulations indicate that no open talik would form below the East Zone and Center Zone pits during the estimated mining and milling operation period of 14 years, although a thin thawed zone would develop surrounding the open pits. For the Main Zone pit where the excavation would break through permafrost, the open talik remains following an extended operation period of 25 years with a 5 to 30 m thawed zone along the pit side walls. In the long term, with a plausible climate change scenario of 5 °C increase in the mean annual ground surface temperature during the next 100 years, the permafrost surrounding the in-pit tailings management facilities (TMFs) would reduce greatly in about 500 years. However, an approximately 40 m thick permafrost layer would remain on the top of the TMFs, which is impervious and would prevent any tailings pore water from migrating upward to reach the ground surface. [ABSTRACT FROM AUTHOR]

Lake ice is a dominant component of Canada's landscape and can act as an indicator for how freshwater aquatic ecosystems are changing with warming climates. While lake ice monitoring through government networks has decreased in the last three decades, the increased availability of remote sensing images can help to provide consistent spatial and temporal coverage for areas with annual ice cover. Synthetic aperture radar (SAR) data are commonly used for lake ice monitoring, due to the acquisition of images in any condition (time of day or weather). Using Sentinel-1 A/B images, a high-density time series of SAR images was developed for Lake Hazen in Nunavut, Canada, from 2015–2018. These images were used to test two different methods of monitoring lake ice phenology: one method using the first difference between SAR images and another that applies the Otsu segmentation method. Ice phenology dates determined from the two methods were compared with visual interpretation of the Sentinel-1 images. Mean errors for the pixel comparison of the first difference method ranged 3–10 days for ice-on and ice-off, while average error values for the Otsu method ranged 2–10 days. Mean errors for comparisons of different sections of the lake ranged 0–15 days for the first difference method and 2–17 days for the Otsu method. This research demonstrates the value of temporally consistent image acquisition for improving the accuracy of lake ice monitoring. [ABSTRACT FROM AUTHOR]

Seabed sediment composition is an important component of benthic habitat and there are many approaches for producing maps that convey sediment information to marine managers. Random Forest is a popular statistical method for thematic seabed sediment mapping using both categorical and quantitative supervised modelling approaches. This study compares the performance and qualities of these Random Forest approaches to predict the distribution of fine-grained sediments from grab samples as one component of a multi-model map of sediment classes in Frobisher Bay, Nunavut, Canada. The second component predicts the presence of coarse substrates from underwater video. Spatial and non-spatial cross-validations were conducted to evaluate the performance of categorical and quantitative Random Forest models and maps were compared to determine differences in predictions. While both approaches seemed highly accurate, the non-spatial cross-validation suggested greater accuracy using the categorical approach. Using a spatial cross-validation, there was little difference between approaches—both showed poor extrapolative performance. Spatial cross-validation methods also suggested evidence of overfitting in the coarse sediment model caused by the spatial dependence of transect samples. The quantitative modelling approach was able to predict rare and unsampled sediment classes but the flexibility of probabilistic predictions from the categorical approach allowed for tuning to maximize extrapolative performance. Results demonstrate that the apparent accuracies of these models failed to convey important differences between map predictions and that spatially explicit evaluation strategies may be necessary for evaluating extrapolative performance. Differentiating extrapolative from interpolative prediction can aid in selecting appropriate modelling methods. [ABSTRACT FROM AUTHOR]