A framework to assess permafrost thaw threat for land transportation infrastructure in northern Canada

Prediction of permafrost stability is associated with challenges, such as data scarcity and climate uncertainties. Here we present a data-driven framework that predicts permafrost thaw threat based on present ground ice distributions and ground surface temperatures predicted via machine learning. Th...

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Bibliographic Details
Published in:Communications earth & environment 2024-12, Vol.5 (1), p.167-13, Article 167
Main Authors: Fatolahzadeh Gheysari, Ali, Maghoul, Pooneh
Format: Article
Language:English
Subjects:
Artificial intelligence
Climate change
Climate models
Climate prediction
Climatic data
Ground ice
Infrastructure
Long short-term memory
Machine learning
Permafrost
Railroads
Surface temperature
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Summary:Prediction of permafrost stability is associated with challenges, such as data scarcity and climate uncertainties. Here we present a data-driven framework that predicts permafrost thaw threat based on present ground ice distributions and ground surface temperatures predicted via machine learning. The framework uses long short-term memory models, which account for the sequential nature of climate data, and predicts ground surface temperature based on several climate variables from reanalysis products and regional climate models. Permafrost thaw threat is then assessed for three cases in northern Canada: Hudson Bay Railway, Mackenzie Northern Railway, and Inuvik–Tuktoyaktuk Highway. The models predict ground surface warming in all studied areas under both moderate and extreme climate change scenarios. The results also suggest that all studied cases are already under threat, with the northern sections of the Hudson Bay Railway and Inuvik–Tuktoyaktuk Highway facing an increasing threat by the end of the century.
ISSN:2662-4435
2662-4435
DOI:10.1038/s43247-024-01317-7