This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: A new study provides insight into the economic impact of melting permafrost on Arctic communities. Permafrost is ground that permanently remains below freezing for at least two consecutive years.
Permafrost covers about 24% of the Northern Hemisphere, including large areas of Alaska, Canada and Siberia. Global warming patterns are causing permafrost to melt. This melting releases previously stored greenhouse gases.
It can also lead to significant infrastructure damage for communities built on permafrost that rely on its structural integrity. This latter impact was the focus of the study published in Earth's Future. This work was led by Elias Manos, a doctoral candidate in the Department of Natural Resources and the Environment (NRE) in the College of Agriculture, Health and Natural Resources (CAHNR), and Chandi Witharana, assistant professor of NRE, alongside collaborators at George Washington University and the Woodwell Climate Research Center.
A previous study looking at this problem considered only buildings' two-dimensional structure. This led to significant underestimates of damage and rebuilding costs. This is especially relevant for Siberian communities, as the Soviet Union constructed many multistory apartment buildings in the region to aid natural resource extraction.
"We can only know the true extent of damage to the built environment if we have a complete representation of the assets that are at risk," Manos says. "We improved the representation of exposure by mapping buildings in three dimensions." When taking a building's three-dimensionality into consideration, the researchers estimate that permafrost melt will cost $261 billion by the middle of the 21st century. This is more than double the previous estimate ($110 billion).
This updated estimate is much higher because the material costs to rebuild multistory buildings are much higher than those for single-story buildings. To calculate this estimate, the researchers used a combination of remote sensing data and artificial intelligence. Using a large 10-terabyte data set of satellite images, the researchers trained a deep learning model to identify buildings.
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