Logistic models as a forecasting tool for snow avalanches in a cold maritime climate: northern Gaspésie, Québec, Canada

F. Gauthier (), D. Germain () and B. Hétu ()
Additional contact information
F. Gauthier: Université du Québec à Rimouski
D. Germain: Université du Québec à Montréal
B. Hétu: Université du Québec à Rimouski

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2017, vol. 89, issue 1, No 10, 232 pages

Abstract: Abstract Snow avalanches are a major natural hazard for road users and infrastructure in northern Gaspésie. Over the past 11 years, the occurrence of nearly 500 snow avalanches on the two major roads servicing the area was reported. No management program is currently operational. In this study, we analyze the weather patterns promoting snow avalanche initiation and use logistic regression (LR) to calculate the probability of avalanche occurrence on a daily basis. We then test the best LR models over the 2012–2013 season in an operational forecasting perspective: Each day, the probability of occurrence (0–100%) determined by the model was classified into five classes avalanche danger scale. Our results show that avalanche occurrence along the coast is best predicted by 2 days of accrued snowfall [in water equivalent (WE)], daily rainfall, and wind speed. In the valley, the most significant predictive variables are 3 days of accrued snowfall (WE), daily rainfall, and the preceding 2 days of thermal amplitude. The large scree slopes located along the coast and exposed to strong winds tend to be more reactive to direct snow accumulation than the inner-valley slopes. Therefore, the probability of avalanche occurrence increases rapidly during a snowfall. The slopes located in the valley are less responsive to snow loading. The LR models developed prove to be an efficient tool to forecast days with high levels of snow avalanche activity. Finally, we discuss how road maintenance managers can use this forecasting tool to improve decision making and risk rendering on a daily basis.

Keywords: Road; Highway; Avalanche forecast; Statistical analysis; Logistic regression (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s11069-017-2959-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:89:y:2017:i:1:d:10.1007_s11069-017-2959-3

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-017-2959-3

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().