Ice-block fall and snow avalanche hazards in northern Gaspésie (eastern Canada): Triggering weather scenarios and process interactions - Graveline, Marie-Hélène; Germain, Daniel (Cold Regions Science and Technology, 2016)
Abstract The database of the Quebec Ministry of Transport allowed us to analyze the occurrence of ice-block falls and snow avalanches for the past decades along national road 132. The results show that ice structure collapse may be categorized into three distinct phases by using daily temperatures (minimum, maximum, and average) and the cumulative degree day (temperatures above 0°C) since the March 1 st , corresponding to the beginning of the ice wall melting period: 1) a short and intense period of ice-block falls from the mid-April to the beginning of May; 2) a period of constant activity, mainly during the two first weeks of May; and 3) isolated residual activity, with a low frequency of ice-block falls until the month of June. The snow avalanche days were mainly characterized by significant snowfalls or rain-on-snow events with temperature>0°C. The multi-hazard probability was then evaluated based on the timing and relative frequency of ice-block fall and the modeling of sufficient snowpack for avalanching. This simple method to assess the synergistic effect of hillslope processes allows a better understanding of the spring avalanche regime related to the collapse of ice structures. These findings are expected to assist in the management of natural hazards and to improve our knowledge of spatiotemporal dynamics of mass-wasting events on highways.

Flood risk quantification and mapping: An integrated vulnerability-based approach at the local scale in New Brunswick, Canada - Duhamel, Francis; Germain, Daniel; Fortin, Guillaume (International Journal of Disaster Risk Reduction, 2022)
This paper explores the risk approach, considering both the physical and human dimensions of the phenomenon in order to produce a more realistic and spatial analysis of risk. Exposure and vulnerability were combined and evaluated multidimensionally, considering individual, socio-economic, and structural (building-related) aspects. These risk factors were then integrated in a multi-criteria analysis in order to produce a comprehensive risk index that could be visualized at the building scale. The relative importance of the indicators was determined through a participatory process involving local and national experts on civil security and flooding. Particular attention was paid to individual vulnerability, including perception and preparedness for flood risk, which were explored directly with local people using a questionnaire. Qualitative and quantitative analyses of the responses allowed for a better understanding of the perception and preparedness of populations exposed to flooding. These data should help to improve risk communication between the authorities concerned and the populations at risk, as well as encouraging implementation of appropriate measures and a bottom-up participatory management approach. The integration of data in a geographic information system enables the visualization and spatialization of risk, but also each of its components.
Keywords: Infolettre; Infolettre_Fevrier_2023

Logistic models as a forecasting tool for snow avalanches in a cold maritime climate: northern Gaspésie, Québec, Canada - Gauthier, F.; Germain, Daniel; Hétu, Bernard (Natural Hazards, 2017)
Snow avalanches are a major natural hazard for road users and infrastructure in northern Gaspesie. 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.