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...

Full description

Bibliographic Details
Main Author: Agyei, Nana Kwabena Frimpong
Other Authors: Hayley, Jocelyn L., Lauer, Rachel Mollie, Papalexiou, Simon
Format: Master Thesis
Language:English
Published: Graduate Studies 2025
Subjects:
Online Access:https://hdl.handle.net/1880/120980
https://doi.org/10.11575/PRISM/48570
_version_ 1831845810513379328
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