Using Spatial Regression as a Tool for Permafrost Hazard Assessment: A Case Study of the Hudson Bay Railway
The Hudson Bay Railway (HBR) has faced increasing instability and rising maintenance costs due to permafrost thaw, a process accelerated by climate change over the past three decades. Geotechnical investigations have identified the Herchmer Subdivision as the most severely impacted area, with histor...
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Other Authors: | , , |
Format: | Master Thesis |
Language: | English |
Published: |
Graduate Studies
2025
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Subjects: | |
Online Access: | https://hdl.handle.net/1880/120980 https://doi.org/10.11575/PRISM/48570 |
_version_ | 1831845810513379328 |
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author | Agyei, Nana Kwabena Frimpong |
author2 | Hayley, Jocelyn L. Lauer, Rachel Mollie Papalexiou, Simon |
author_facet | Agyei, Nana Kwabena Frimpong |
author_sort | Agyei, Nana Kwabena Frimpong |
collection | PRISM - University of Calgary Digital Repository |
description | The Hudson Bay Railway (HBR) has faced increasing instability and rising maintenance costs due to permafrost thaw, a process accelerated by climate change over the past three decades. Geotechnical investigations have identified the Herchmer Subdivision as the most severely impacted area, with historical and contemporary data revealing that previously stable ground is becoming unstable and that the permafrost boundary is shifting northward. As permafrost degradation continues, there is a pressing need for accurate predictions of thaw-related hazards to support infrastructure resilience and maintenance planning along the HBR. To address this challenge, we employed Multiscale Geographically Weighted Regression (MGWR) to identify the key variables contributing to sinkhole formation along the railway. This spatial modeling tool enables the assessment of multiple climatic and ecological factors influencing permafrost degradation while determining their statistical significance. In our MGWR model, sinkholes—measured by the number of surface depressions along the railway—served as the dependent variable. In contrast, the independent variables included ground ice abundance, snow depth, surface temperature, and organic carbon content. Regression coefficients derived from the MGWR model were used to calculate variable weights, which were then applied in a weighted sum analysis in ArcGIS to generate a hazard map. This hazard map incorporates projected datasets for the 2030–2039 period, offering insights into the evolving risk landscape along the railway. By illustrating the shifting permafrost boundaries, the map enhances our understanding of the railway’s vulnerability to damage caused by the thawing permafrost. The findings from this study will serve as a crucial tool for guiding resource allocation and mitigation strategies, ensuring the long-term stability of the HBR in the face of climate change. |
format | Master Thesis |
genre | Hudson Bay Ice permafrost |
genre_facet | Hudson Bay Ice permafrost |
geographic | Herchmer Hudson Hudson Bay |
geographic_facet | Herchmer Hudson Hudson Bay |
id | ftunivcalgary:oai:ucalgary.scholaris.ca:1880/120980 |
institution | Open Polar |
language | English |
long_lat | ENVELOPE(-94.170,-94.170,57.371,57.371) |
op_collection_id | ftunivcalgary |
op_doi | https://doi.org/10.11575/PRISM/48570 |
op_relation | Agyei, N. (2025). Using spatial regression as a tool for permafrost hazard assessment: a case study of The Hudson Bay Railway (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. https://hdl.handle.net/1880/120980 https://dx.doi.org/10.11575/PRISM/48570 |
op_rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. |
publishDate | 2025 |
publisher | Graduate Studies |
record_format | openpolar |
spelling | ftunivcalgary:oai:ucalgary.scholaris.ca:1880/120980 2025-05-11T14:20:42+00:00 Using Spatial Regression as a Tool for Permafrost Hazard Assessment: A Case Study of the Hudson Bay Railway Agyei, Nana Kwabena Frimpong Hayley, Jocelyn L. Lauer, Rachel Mollie Papalexiou, Simon 2025-04-01 application/pdf https://hdl.handle.net/1880/120980 https://doi.org/10.11575/PRISM/48570 en eng Graduate Studies University of Calgary Agyei, N. (2025). Using spatial regression as a tool for permafrost hazard assessment: a case study of The Hudson Bay Railway (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. https://hdl.handle.net/1880/120980 https://dx.doi.org/10.11575/PRISM/48570 University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. Permafrost Hazards Linear Infrastructure Hudson Bay Railway Engineering--Civil master thesis 2025 ftunivcalgary https://doi.org/10.11575/PRISM/48570 2025-04-15T14:27:25Z The Hudson Bay Railway (HBR) has faced increasing instability and rising maintenance costs due to permafrost thaw, a process accelerated by climate change over the past three decades. Geotechnical investigations have identified the Herchmer Subdivision as the most severely impacted area, with historical and contemporary data revealing that previously stable ground is becoming unstable and that the permafrost boundary is shifting northward. As permafrost degradation continues, there is a pressing need for accurate predictions of thaw-related hazards to support infrastructure resilience and maintenance planning along the HBR. To address this challenge, we employed Multiscale Geographically Weighted Regression (MGWR) to identify the key variables contributing to sinkhole formation along the railway. This spatial modeling tool enables the assessment of multiple climatic and ecological factors influencing permafrost degradation while determining their statistical significance. In our MGWR model, sinkholes—measured by the number of surface depressions along the railway—served as the dependent variable. In contrast, the independent variables included ground ice abundance, snow depth, surface temperature, and organic carbon content. Regression coefficients derived from the MGWR model were used to calculate variable weights, which were then applied in a weighted sum analysis in ArcGIS to generate a hazard map. This hazard map incorporates projected datasets for the 2030–2039 period, offering insights into the evolving risk landscape along the railway. By illustrating the shifting permafrost boundaries, the map enhances our understanding of the railway’s vulnerability to damage caused by the thawing permafrost. The findings from this study will serve as a crucial tool for guiding resource allocation and mitigation strategies, ensuring the long-term stability of the HBR in the face of climate change. Master Thesis Hudson Bay Ice permafrost PRISM - University of Calgary Digital Repository Herchmer ENVELOPE(-94.170,-94.170,57.371,57.371) Hudson Hudson Bay |
spellingShingle | Permafrost Hazards Linear Infrastructure Hudson Bay Railway Engineering--Civil Agyei, Nana Kwabena Frimpong Using Spatial Regression as a Tool for Permafrost Hazard Assessment: A Case Study of the Hudson Bay Railway |
title | Using Spatial Regression as a Tool for Permafrost Hazard Assessment: A Case Study of the Hudson Bay Railway |
title_full | Using Spatial Regression as a Tool for Permafrost Hazard Assessment: A Case Study of the Hudson Bay Railway |
title_fullStr | Using Spatial Regression as a Tool for Permafrost Hazard Assessment: A Case Study of the Hudson Bay Railway |
title_full_unstemmed | Using Spatial Regression as a Tool for Permafrost Hazard Assessment: A Case Study of the Hudson Bay Railway |
title_short | Using Spatial Regression as a Tool for Permafrost Hazard Assessment: A Case Study of the Hudson Bay Railway |
title_sort | using spatial regression as a tool for permafrost hazard assessment: a case study of the hudson bay railway |
topic | Permafrost Hazards Linear Infrastructure Hudson Bay Railway Engineering--Civil |
topic_facet | Permafrost Hazards Linear Infrastructure Hudson Bay Railway Engineering--Civil |
url | https://hdl.handle.net/1880/120980 https://doi.org/10.11575/PRISM/48570 |