Ice-block fall and snow avalanche hazards in northern ...

Cold Regions Science and Technology 123 (2016) 81–90

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Ice-block fall and snow avalanche hazards in northern Gaspésie (eastern Canada): Triggering weather scenarios and process interactions Marie-Hélène Graveline 1, Daniel Germain ⁎ Université du Québec à Montréal, Département de Géographie, C.P. 8888, succursale Centre-ville, Montréal, QC, Canada, H3C 3P8

a r t i c l e

i n f o

Article history: Received 7 June 2015 Received in revised form 16 October 2015 Accepted 15 November 2015 Available online 10 December 2015 Keywords: Ice-block falls Snow avalanches Hillslope processes Degree day Process interaction

a b s t r a c t The database of the Québec 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 1st, 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 N 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. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Due to their proximity to steep slopes, most roads in mountainous environments are exposed to various geomorphic hazards (Baillifard et al., 2003; Budetta, 2004; Bündl et al., 2004; Devkota et al., 2013; Hétu, 2007). These hazards may affect both the physical safety of individuals, infrastructure and facilities as well as the well being of surrounding communities (Bunce et al., 1997; Dalziel and Nicholson, 2001; Das et al., 2010). However, little research has been done on the synergistic effects of different geomorphic processes, emphasizing the need to consider not only the spatial extent (i.e. run-out distance and lateral spread) of mass movements, but also their return intervals and timing of occurrence. In northern Gaspésie, rapid mass movements are known to occur and have been studied for a long time, particularly on scree slopes (i.e. see the review of Hétu and Gray, 2000). Indeed, the observations made over the past decades have revealed a large variety of active hillslope processes, such as snow avalanches, ice-block falls, rockfalls, hyperconcentrated flows, etc. (Gauthier et al., 2012; Germain et al., 2010; Hétu, 1991, 1995; Ouellet and Germain, 2014). Along the coast, numerous snow avalanches and ice-block falls are reported by the Québec Ministry of Transport (MTQ) each winter as causing disturbances to ⁎ Corresponding author. Tel.: +1 514 987 3000 # 7096; fax: +1 514 987 6784. E-mail address: [email protected] (D. Germain). 1 Tel.: +1 514 987 3000 # 7096; fax: +1 514 987 6784.

http://dx.doi.org/10.1016/j.coldregions.2015.11.012 0165-232X/© 2015 Elsevier B.V. All rights reserved.

the road traffic of the main transportation corridors (Fortin et al., 2011; Gauthier et al., 2012; Hétu, 2007). Although several studies have been undertaken to investigate the distribution, frequency and impacts of snow avalanche activity in the Chic-Chocs Mountains in central Gaspésie (Boucher et al., 2003; Germain et al., 2009, 2010; Larocque et al., 2001) and in the low-elevated coastal valleys of the northern Gaspésie (Dubé et al., 2004; Fortin et al., 2011; Germain et al., 2005; Hétu, 1991, 2007; Hétu and Vandelac, 1989), no efficient avalanche forecasting program has yet been developed. Germain et al. (2009) identified five probable weather scenarios responsible for major snow avalanche activity at the regional scale through a dendrogeomorphic approach. Dubé et al. (2004) reported that the years of high snow avalanche activity along the coast were characterized by snowy winters, with total snowfall well above average. Hétu (2007) and Fortin et al. (2011) highlighted more specifically that snow avalanche activity occurs following two distinct regimes: i) a winter regime, closely associated with a snowstorm event, or the amount of precipitation received the day of the event or during the two previous days leading up to the event and; ii) a spring regime related to a rainon-snow event, a significant rise in temperatures above 0 °C, or the occurrence of ice-block falls. In northern Gaspésie, ice-block falls come from the melting and collapse of ice structures growing over the cliffs during the cold season. According to Bianchi (2004) and Gauthier et al. (2015a, 2012), these ice formations can be classified as frozen waterfalls (ice cascades) or ice walls depending on their hydrological regime, namely the freezing of

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M.-H. Graveline, D. Germain / Cold Regions Science and Technology 123 (2016) 81–90

surface runoff or ground water seeping over a cliff face. However, iceblock fall activity appears mainly related to the melting and collapse of ice walls rather than the frozen waterfalls (Gauthier et al., 2015a). Until the recent synthesis of Gauthier (2013), only a few studies had described either the genesis or collapse of rockwall icings (Bianchi, 2004; Montagnat et al., 2010; Weiss et al., 2011). Thermodynamic and complete energy balance models have subsequently been developed (Gauthier et al., 2013, 2015a), but these complex models require highprecision meteorological data in the immediate surroundings of ice structures as well as the monitoring of ice volume changes. Instead of these complex models, it has also been shown that the accumulation of positive air temperature—known as the melting degree day—can be used to explain the growth and decay of ice structures (Gauthier et al., 2013, 2015b; Montagnat et al., 2010). The hazards related to hillslope processes such as snow avalanches and ice block falls, as complex and different they are from each other, are generally addressed individually. However, recent observations in the northern Gaspésie confirm the presence of distinct mass movements, snow avalanches and ice-block falls notably, within the same path (Hétu, 2007). It therefore appears important to focus on the dynamics of hillslope processes, but also to consider the synergistic effects (i.e. spatiotemporal interactions) between these processes to improve risk assessment and management. The objectives of this case study were twofold: 1) to characterize ice-block fall and snow avalanche events and related weather conditions; and 2) to identify the timing and probabilities of occurrence for each process and the period of joint activity. 2. Region and study site 2.1. Climate, geology and geomorphology The northern Gaspésie is located in a mid-latitude zone (49° N) belonging to the cold-temperate Koppën classification (Dfb class), which is influenced both by Arctic air masses and the depressions from the Atlantic coast of the United States. The mean annual temperature is approximately 3.1 °C and monthly minimum (January) and maximum (July) temperatures averaged −15.6 °C and 20.6 °C, respectively. The annual precipitation totals about 800 mm, with 30 to 40% falling as snow between the months of October and April (Environnement Canada, 2012). Because of the influence of oceanic air masses and thermal inversion due to the orographic effect, snowstorms, blizzards, rain, and periods of extreme warm and cold can be expected in any month during the snow season (e.g. Fortin and Hétu, 2014). The rapid succession of these contrasting meteorological contexts creates a complex snowpack on which little information exists due to the absence of a program to monitor the physical properties of the snow cover (Fortin and

Hétu, 2009; Fortin et al., 2011). However, a recent study using a combination of temperature and precipitation data over the last few decades did not show any clear evidence or trend of climate change in this area (Fortin and Hétu, 2014). The regional landscape consists of an imposing forest plateau, mostly between 300 and 400 m above sea level and surrounded by a large coastal escarpment facing north (Fig. 1). The geologic formations consist mainly of clayshale, silty claystone, greywacke and mudstone (Brisebois and Brun, 1994). These formations are very friable and characterized by a network of fractures in which groundwater discharge is responsible for massive ice-wall formation in the upper part of many scree slopes. As reported by Gauthier et al. (2015b, 2012), the first frost usually occur in October but the ice wall formation really begins around midNovember. 2.2. National Road 132 and the study site Between Sainte-Anne-des-Monts and Manche-d'Épée (Fig. 1), national road 132 is bordered by steep slopes and the St. Lawrence River over nearly 80% of its length. Average daily traffic varies between only 1 and 499 vehicles near less populated areas, and 600 to 2000 vehicles in the area of Sainte-Anne-des-Monts (Transport Québec, 2004), however this road is the main transportation corridor in the northern Gaspésie (Fig. 2). Despite a limited period of exposure of road-users to hillslope hazards, the Service Centre of Sainte-Anne-des-Monts of the Quebec Ministry of Transport (MTQ) started an inventory program in 1987 to report each intervention (e.g. removal of snow, rockfalls, landslides and ice debris from the road). The site studied (Aqua Velva) is the largest of the seven ice walls located along the sector 60 (Gauthier et al., 2015b), near Cap-au-Renard (Fig. 1). The path has a length of 200 m and an average width of 7.3 m. Facing north, it has an average gradient of 34.8°, with a subvertical escarpment of 60 m in the upper part, which is covered every winter with an impressive thick shell of ice (~4500 m3) related to a resurgence of ground water (Fig. 2a, b). In the spring, the collapse of this ice structure releases blocks of ice with a diameter of up to 2 to 3 m (Fig. 2c). The presence of a ditch and a small retaining wall along the road is not sufficient to stop large blocks of ice and snow avalanches (Fig. 2c–e). 3. Methods 3.1. Ice-block falls and snow avalanches database The database used in this study is part of a larger monitoring and inventory program of the MTQ, which started in 1987. The MTQ subdivided road 132 into 12 distinct sections (10, 50 to 130; see

Fig. 1. Location of the study area (insert) and inventory sectors by the Québec Ministry of Transport (MTQ) on the national road 132 in the northern Gaspé Peninsula. The study site Aqua Velva is located in road sector 60, near Cap-au-Renard. Source: Hétu (2007).


a

83

c

d

b

e

Fig. 2. a) Ice wall partially collapsed and dirty wet snow avalanche deposit on April 30th 2008; b) snow avalanche deposit at the end of March 2010; c) impacted small retaining wall along the national road 132 by large ice block falls on May 15th 2005; d) snow avalanche deposit with several blocks of ice recovering half of the national road 132 on April 24th 1991; and e) small avalanche deposit covering the shoulder of the road on March 31st 2006. The photos in a and c are courtesy of F. Gauthier (cf. Gauthier, 2008; Gauthier et al., 2012). The photos d and e are courtesy of the Québec Ministry of Transport (cf. Hétu, 2007). The photo b is from D. Germain.

Fig. 1) varying in length between 4.1 and 11.8 km. Unfortunately, the inventory remains incomplete for some years. Although since 2000 these forms are computerized, for unknown reasons, the 2001 data are completely missing as well as for snow avalanches for which no data are available before 2000. The information provided on these forms is limited; however they do indicate the date of the given event, type of hazard (i.e. snow avalanche, ice-block fall, rockfall, etc.) and the type of intervention (i.e. manual or mechanized). Obviously, this means that small ice-block falls, as well as snow avalanches whose spatial extent does not reach the road, are not recorded (Hétu, 1991, 2007). Therefore, only major collapses of rockwall icings and large snow avalanches that cause disturbances to the road traffic are recorded. Among the seven ice walls recorded along the sector 60, the most problematic site is definitely Aqua Velva as confirmed by the MTQ staff and Gauthier et al. (2015b). However, because the exact location of the interventions is not performed, no distinction between the different paths of the same sector is possible. In this regard, all the data in the sector 60 has been considered in this study as being related to the site Aqua Velva except when more than one snow avalanche occurred on

the same day, such as February 2nd 2010, where six snow avalanches were recorded by the MTQ. 3.2. Meteorological data and analysis The meteorological data are from Environment Canada weather stations in Cap-Chat (49° 06′ N, 66° 39′ W, 5 m a.s.l.) and Sainte-Anne-desMonts (49° 08′ N, 66° 28′ W, 15 m a.s.l.), the first one being located 10 km west of the second weather station (Fig. 1). Daily temperatures (minimum, maximum and average) and precipitations (liquid, solid and total) data were used. 3.2.1. Ice-block fall activity The analysis of average daily temperatures (minimum, maximum and average) from March until June, and their links with the events listed in the MTQ database for the period 1987–2011, allowed us to specify the role of temperature in the collapse of Aqua Velva ice wall. This analysis was performed over 13 years for which the MTQ recorded ice-block fall disturbances, namely 1987, 1989–1992, 2000, 2003, 2005,

84


2006 and 2008–2011. The period of activity was determined by calculating the cumulative degree day (DD), corresponding to the sum of the average daily temperatures above 0 °C, since March 1st. The choice of March 1st is related to the fact that this is typically the beginning of the ice wall melting period corresponding to average maximum temperatures above 0 °C (Gauthier et al., 2012, 2015b). After calculating the number of DD for each event, a cumulative distribution function based on the relative frequency of ice-block fall occurrence was built. The ice-block fall activity based on the MTQ database, weather conditions (DD and temperatures) and field observations from Aqua Velva was then compared to the recent advances and knowledge about the melting and collapse of rockwall icings (Gauthier et al., 2015a, b, 2013, 2012). Finally, in order to get a better prospective view, the period of ice-block fall activity based on DD was extrapolated to the weather historical data for the period 1935–2011, excluding 1975 due to missing meteorological data, to detect any trends. 3.2.2. Snow avalanche activity For snow avalanches, analyses were performed over a period of 10 years (2000–2011) and are based on the methodology of Fortin et al. (2011). These analyses were carried on the following aspects: i) liquid, solid, and total precipitation accrued over 24, 48, and 72 h, respectively; and ii) the statistical distribution of winter precipitation (from December to April) and estimated snow thickness on the ground. Snow avalanche activity was classified according to the following categories: i) a winter (snowstorm) regime, related to the amount of snow accumulated the day of the event or during the two days preceding the event; and ii) a spring regime, related to rain-on-snow-events and a significant rise in temperature above 0 °C or the occurrence of ice-block falls (Fortin et al., 2011; Hétu, 2007). The snowstorm avalanche regime was determined using the amount of snow precipitation (cm) accrued over 24, 48, and 72 h, using three classes (class 1: 15 ≤ 25 cm; class 2: 25 ≤ 35 cm; class 3: N 35 cm). The frequency and distribution of these classes—hereafter labeled as potential avalanche days (PAD)—were then observed in the meteorological data for the period 2000–2011. The spring avalanche regime was determined using the temperatures ≥ 0 °C and rainfall occurrence. However, because iceblock falls may also trigger snow avalanches (cf. Hétu, 2007), we also used a threshold of 50 cm of snow thickness based on field observations of ground roughness as an essential condition for an avalanche to occur. In this regard and because the meteorological station did not provide information on snow thickness, the latter was estimated for the period 2000–2011 by subtracting the thickness of the melted snow cover (M) (cm) from the cumulative snow precipitation (cm). This implies that every centimeter of snow precipitation was considered to have contributed to the snowpack. The thickness of the melted snow cover was estimated following the model of Rango and Martinec (1995), which is based on the melting degree day (MDD): M ¼ aT

ð1Þ

where a represents the coefficient of MDD in cm/DD and T the MDD in °C/day. The coefficient a was determined using the following equation: ps −1 −0:2392 ¼ 1:96 a cm C −1 d pw

ð2Þ

where ps and pw represent the density of the snow and of the water, respectively. Because very little information is available about the snowpack characteristics in the northern Gaspésie (Fortin and Hétu, 2009; Germain et al., 2010), similar values recorded in other maritime areas were used (e.g. 435 kg/m3 in March; 455 kg/m3 in April, and 520 kg/m3 in May), such as reported in western Canada (Mount Seymour) and Norway (Larsen, 2000). The MDD (T; °C/day) of Eq. 1 was calculated from the beginning of the intermittent melting period

(p), which corresponds to the first six consecutive days when the average daily temperature is above 0 °C (Yagouti et al., 2008). The daily snow cover thickness was calculated by subtracting the cumulative thickness of the melted snow cover (M) (cm) from the cumulative solid precipitation (cm). The melting rate (cm/day) was then calculated by dividing the annual amount of solid precipitation (cm) by the number of days in the intermittent melting period (Ouellet and Germain, 2014). 3.2.3. Multi-hazard probability In addition to the meteorological analysis of the triggering conditions of ice-blocks fall and snow avalanche events described previously, we also evaluated the probability of synergistic effects between these two hillslope processes. In that regard, only solid and total precipitation were used and the calculation of the daily ratio (%) between snow cover (cumulative thickness in cm) and cumulative total precipitation (water equivalent in mm), allowed us to determine the coincidence of a sufficient snow cover thickness (≥50 cm) with the period of ice-block falls based on the DD. 4. Results A total of 112 interventions related to hillslope hazards have been recorded by the MTQ in road section 60 between 1987 and 2011. Of these, 58.0% were related to rockfalls, 26.8% to ice-block falls and 15.2% to snow avalanches. Rockfalls were not taken into account in this study because they occurred mainly outside of the cold season, and the digging of a ditch in late 1990s significantly reduced the number of necessary interventions. On the contrary, ice-block falls and snow avalanches have been associated to the study site Aqua Velva, the latter being the only major ice wall in sector 60 as noted earlier (i.e. Gauthier et al., 2015b). 4.1. Ice-block fall occurrence and probability A total of 30 interventions related to ice wall melting and collapse were recorded by the MTQ. Of these 30 events, nearly half resulted in a mechanized operation (Fig. 3a). The years 1991, 2005, 2008, and 2010 are above the average of two annual interventions, with six, three, five, and five interventions respectively (Fig. 3b). It should be noted that for 53.8% of the years for which the MTQ recorded activity, this was only one active day. The period of activity can range from March until the beginning of June as reported by Gauthier et al. (2015b, 2012), although no event was recorded in March at the study site. However, the months of April (34%) and May (63%) concentrate 97% of the interventions (Fig. 3a). The analysis of temperatures and cumulative degree day (DD) since the March 1st (i.e. as per Gauthier et al., 2012) revealed a collapse of the entire ice structure in three different phases (Fig. 4). The first phase of activity corresponds to an average daily temperature ≥ 0 °C—with minimum temperature usually below the freezing point—and ranges between 35 and 100 DD, with 53.3% of relative frequency of the 30 iceblock events from Aqua Velva (Fig. 4). The rapid increase in the relative frequency was characterized by fast and intense ice-block fall activity, particularly between 80 and 100 DD (Fig. 4). However, an average of 17 days with a temperature above 0 °C is necessary before a first intervention takes place (Fig. 5a), corresponding to a minimum of 35 DD (Fig. 5b). According to the weather data (1935–2011), this 65-DD period corresponded to the period from April 21st to May 6th (±1 standard deviation (SD) of 76.3% and 80.3% respectively). The average length is 16 days, but it can range between eight and 29 days. The observation of historical data showed that it is likely to range between 13 and 19 days in total (63.2% of cases). The observations of the beginning and end of this first phase of activity revealed that in 48.7% of cases, it begins between April 20th and 27th and ends between May 4th and 9th.

2

2011

2010

June

2009

May

2006

April

2008

0 March

2005

1 2000

0

3

2003

2

4

1992

4

85

5

1991

6

6

1989

8

7

1990

10

b

1987

12

Number of intervention

a Number of intervention


Fig. 3. Monthly (a) and yearly (b) distribution of mechanized (black) and manual (gray) interventions related to the 30 ice-block fall events recorded by the MTQ for the sector 60 on the national road 132. Only the years for which the intervention of the MTQ was requested are shown.

The second phase of activity is characterized by a positive daily minimum temperature (Tmin ≥ 0 °C), corresponding roughly to 100 DD despite the inter-annual variability, and ranges between 100 and 185 DD (Fig. 4). This interval includes 23.3% of relative frequency of events between 1987 and 2011, which is lower than the first phase of activity. This phase lasts on average from May 6th to 19th (±1 SD in 80.3% and 77.6% respectively). The average length is 14 days, but it can range between nine and 21 days. However, there is a 76.3% probability that this phase lasts 12 to 16 days (23.7% for a period of 15 days). It also starts mainly between May 4th and 9th (48.7% of cases) and ends between May 12th and 22th (69.7% of cases). The third and final phase takes place between 185 and 345 DD corresponding on one hand to a lesser slope gradient in Fig. 4 and, on the other hand to the last intervention recorded by the MTQ (Fig. 5b). The frequency of events is much less significant and less consistent (23.3% of the events during a period of 160 DD), reflecting late, isolated activity corresponding to the detachment of residual ice-blocks (Fig. 5b). This phase lasts on average from May 19th to June 5th (±1 SD in 77.6% and 73.7% respectively). The average length is 18 days (minimum and maximum of 6 and 25 days, respectively), but in 71.1% of cases, this phase lasts 15 to 19 days. It also starts mainly between May 12th and 22th (69.7% of cases) and ends between May 30th and June 8th (73.7% of cases).

4.2. Snow avalanche occurrence and probability Seventeen interventions related to snow avalanches were recorded between 2000 and 2011 for the sector 60 on the national road 132. 94% of these snow avalanches required a mechanized operation (Fig. 6a). The interventions usually extend from February to May, but there is more activity in February, with 75.5% of total interventions and 76.4% of the mechanized operations. For the same period, five winters are characterized by snow avalanche activity recorded by the MTQ: 2002–03, 2004–05, 2005–06, 2008–09 and 2010–11 (Fig. 6b). Note that with nine interventions, the winter of 2008–09 is well above the average of three interventions. Table 1 shows the weather conditions for snow avalanches recorded by the MTQ, corresponding to ten avalanche days. Eight of these avalanche days (i.e. 16 snow avalanches) causing road disturbances occurred during February and March. A few of them are characterized by important snowfall (snowstorm) over a short period of time (24, 48 or 72 h), such as March 9th 2005 and February 17th 2006 (Table 1). Although 60% of avalanche days received snowfall during the 72 h preceding the event, and 50% in the preceding 48 or 24 h, some avalanches occurred without any precipitation or warming of temperatures during the previous 3-day period (e.g. February 11th 2003) (Table 1). Considering the small dataset, it is not surprising that no significant statistical

1.0 0.9

Relative frequency

0.8 0.7 0.6 0.5 0.4 Phase 3 23.3 %

0.3 0.2 0.1

Phase 1 53.3 %

Phase 2 23.3 %

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340 350 360 370 380 390 400

0.0 Degree day (°C)

Fig. 4. Relative frequency of the Aqua Velva ice-wall collapse expressed as a cumulative distribution function based on the 30 ice-block fall events recorded by the MTQ for the period 1987– 2011. The three phases are also shown as well as their respective frequency of ice-block falls. The x-axis represents the cumulative degree day (T N 0 °C) since March 1st.

a

Interventions on road 132 T min T max T avg

Interventions

3

25

DD min DD max

15

5 0

DD avg

3

-5

1

Interventions on road 132

b

20

10 2

4

Interventions

4

2

1

-10 0 March/01

-15 April/01

May/01

June/01

0 March/01

April/01

May/01

750 700 650 600 550 500 450 400 350 300 250 200 150 100 50 0

Degree day (°C)


Temperature (°C)

86

June/01

Fig. 5. Temperatures (a) and cumulative degree day of thaw (b) since the 1st March with the frequency of intervention by the MTQ. Minimum and maximum temperatures as well as minimum and maximum cumulative degree day represent the inter-annual variability of the weather conditions for the period 1987–2011.

have been associated with observed snow avalanches (Table 1). For a 24-h period, half of the PAD was associated with a snow avalanche recorded in the MTQ database (50% of relative frequency). This indicates, on one hand, that extreme snowfalls (Class 3) increase the probability of large snow avalanche occurrence and, on the other hand, that the period over which snow accumulates is also important. Indeed, the snow avalanche relative frequency occurrence doubles when going from 72 to 48 h and again from 48 to 24 h. The avalanche days of April 24th 1991, February 28th 2009 and May 4th 2011 appear related to the spring regime with rainfall (e.g. rainon-snow-event) and significant rise of temperatures (e.g. March 31st 2006) above the freezing point (Table 1). In this respect, Table 3 shows the occurrence of these potential causes-of-release where the air temperature warming (Tmin N 0 °C) is expected on average eight and 10 days in December and March, respectively. However, this situation can be expected in any month during the snow season. Rainfall episodes were also reported for each month of the cold season, and particularly in December and March, although the average rainfall appears a little more important and less variable in February (Table 3). The melting degree day (MDD) allowed an estimate of the thickness of the daily snow cover with a threshold of 50 cm, which was considered adequate to discriminate between a snowpack with or without a probability of avalanching. In that regard, snow avalanche season lasts

discrimination between avalanches days and non-avalanche days was found. The analysis of major snowfall over 24, 48 and 72 h, considered here as potential avalanche days (PAD) (cf. Fortin et al., 2011), is presented in Table 2. The PAD of class one (15 ≤ 25 cm) occurred more frequently in December and the number of events decreases slowly as the cold season progresses. PAD of classes two (N25 ≤ 35 cm) and three (N35 cm) show a different pattern with higher frequency in March (Class 2: 67, 50 and 47% over 24, 48, and 72 h) and February (Class 3: 50, 43 and 38% over 24, 48, and 72 h). It should also be noted that on average there is one day per year of extreme snowfalls (N 35 cm in less than 72 h) but some of these were recorded even in April (Table 2). Despite the fact that a large number of PAD were related to the first two classes of precipitation, none of these resulted in interventions recorded by the MTQ. It should be mentioned that small avalanches could have occurred, but they likely did not reach the road. However, several PAD of class three correspond to a snow avalanche recorded by the MTQ at 24, 48, and 72 h prior to the event. Of the 13 PAD characterized by more than 35 cm of snowfall in 72 h (Table 2), two were associated with avalanches (March 9th 2005 and February 17th 2006, two avalanches each; Table 1), corresponding to a relative frequency of occurrence of 15.4%. When limited to cumulative snowfall accrued over 48 h, the relative frequency increases to 28.6%, as two of seven PAD

2

11

9 80

20 10 -

6 50

20 0

09 19 9

5

0 1

0

4

20 0

2

6

40

4

8

3

6

10

20

8

12

20 0

10

b

20 0

12



a

Fig. 6. Monthly (a) and yearly (b) distribution of mechanized (black) and manual (gray) interventions related to the 17 snow avalanches recorded by the MTQ for the sector 60 on the national road 132. Only the years for which the intervention of the MTQ was requested are shown. The avalanche event of April 24th 1991 reported by Hétu (2007) and for which the intervention of the MTQ was necessary is also shown.

M.-H. Graveline, D. Germain / Cold Regions Science and Technology 123 (2016) 81–90 Table 1 Weather conditions related to the 18 snow avalanches recorded by the MTQ along the sector 60 on national road 132. Date

Avalanche (n)

1991–04–24 2003–02–11 2005–02–11 2005–03–09 2006–02–17 2006–03–31 2009–02–10 2009–02–23 2009–02–28 2011–05–04 a

1 1 1 2 2 1 6 2 1 1

Tempa (°C)

Snowfall2 (mm)

Rainfall (mm)

24 h

48 h

72 h

7 11.4 43.5

10 44.1 44.5

10 45.1 45.8

7 2.4

7 2.4

8 8 2.4

Tmn3.0

Tmx9.3 Tmn0.7

Tmx1.1 Tmn2.2

24 h 0

48 h 0.4

72 h 0.8

0 27

5.6 30

5.6 30

Only temperatures above 0 °C are shown: Tmn = minimum temperature and Tmx =

maximum temperature. b EE = water equivalent. Years highlighted in gray correspond to the spring regime with rain-on-snow events or Tmn above 0 °C.

on average from January 1st to May 14th. However, in 63.4% of the years, the season started between December 16th and January 5th, and ended with a probability of 87.3% in mid-May. The earliest snow avalanche season recorded (snowpack ≥ 50 cm) in the historical data was on December 5th, showing once again the importance of inter-annual variability in this cold-temperate climate with strong maritime influences. 4.3. Hillslope process interactions According to Hétu (2007), the fall of an ice-block of 9 m3 triggered a major wet snow avalanche on March 31st 2006 (Fig. 7a–c). This incident demonstrates the existence of a causal link between these processes, at least during a certain period of the year. At the beginning of the collapse of the ice structure (phase 1 with 53.3% of probability; April 21st to May 6th), historical weather data showed the presence of a sufficient snowpack ≥ 50 cm for snow-avalanche occurrence for almost every years (Fig. 8). Even during the second phase (May 6th to 19th), with a lower probability of ice-block falls (23.3%), the thickness of the snowpack appeared sufficient most of the time with a decreasing probability from 74 to 45% (Fig. 8). Conversely, the third phase corresponds to a significant decrease in the snowpack (from 45 to 11%), reducing the probability of a multi-hazard event (Fig. 7d). 5. Discussion 5.1. Ice-block fall and snow avalanche occurrence: major causes-of-release Nearly half of the MTQ interventions between 1987 and 2011 were associated with ice-block falls and snow avalanches. Based on these results, ice-block falls appear to be a geomorphic process causing road disturbance at the study site with an almost yearly recurrence (13 of the 18 years). Since ice walls form every year in this type of climate, years without any event recorded in the MTQ database correspond to ice-block falls that did not reach the road for those years. The

Table 2 Monthly major snowfall occurrences for the period 2000–2011.

24 h

48 h

72 h

a

Classa

Dec

Jan

Feb

March

April

Total

1 2 3 1 2 3 1 2 3

6 0 0 13 1 0 20 3 0

2 0 1 8 0 1 15 0 1

2 1 1 6 3 3 9 5 5

3 2 0 5 5 2 8 7 4

3 0 0 2 1 1 2 0 3

16 3 2 35 10 7 54 15 13

Snowfall precipitations: Class 1: 15 ≤ 25 cm; Class 2: N25 ≤ 35 cm; Class 3: N35 cm.

87

stability of the ice structure is directly related to its cohesion to the rockwall (Gauthier et al., 2015a, 2013). Therefore, the melting period begins when the air temperature rises above the freezing point and the first warm days lead to ice melting from convective heat transfer and solar radiation, which result in the alteration of the ice texture that in turn helps to accelerate the melting (Gauthier et al., 2015a, 2013). Based on the DD from March 1st and daily temperatures, the ice structure collapse may be categorized into three distinct phases (Fig. 6): 1) a short (from the mid-April to the beginning of May) and intense period of ice-block falls (53.3% of relative frequency), characterized by a range of DD from 35 to 100; 2) a period of constant activity (23.3% of relative frequency), mainly during the two first weeks of May (i.e. from 100 until 185 DD); and 3) isolated residual activity, with a low frequency of ice-block falls (23.3% of relative frequency) until the month of June, corresponding to 185 to 345 DD. These phases are in accordance with the previous results of Gauthier et al. (2012) at a regional scale, which recorded an increase in MTQ interventions around 25 DD, a period of intense activity beyond 70 DD and residual activity after 200 DD. However, at the study site Aqua Velva, the duration of ice wall collapse which lasts on average between 40 and 50 days (with 72.4% of probability), appears shorter than elsewhere in northern Gaspésie (~74 days; Gauthier, 2008). The release of snow avalanches is highly variable on a yearly basis (i.e. nine snow avalanche days between 2000 and 2011). However, once they occurred, they required the highest degree (88.2%) of mechanized interventions. Despite the reduced period of inventory and the small number of avalanche events recorded (10 avalanche days and 18 interventions of the MTQ considering the avalanche of April 1991), the results presented in this study are consistent with the two previously weather-related snow avalanche regimes reported by Hétu (2007) in northern Gaspésie: 1) A winter regime, related to important snowfalls and resulting in an overloading effect. The role of the amount and intensity or snowfall in increasing the probability of snow avalanches has been extensively studied and pointed out by several researchers since Atwater (1954). Indeed, Dubé et al. (2004) and Fortin et al. (2011) had already stated the importance of snowfall for snow avalanche occurrence in the northern Gaspésie. At the studied site, an increased probability of snow avalanche occurrence is associated with increasingly heavy or intense snowfall events (N35 cm), particularly from 72 to 48 h and 48 to 24 h. While Hétu (2007) pointed out that the snow avalanche season can last up to seven months, from October to May, the historical weather data (1935–2011) show that the PAD with major snowfall (≥35 cm in less than 72 h) occurs mainly between January and March, most of these PAD were recorded from February to April for the period 2000– 2011 (12/13, 92%; Table 2). 2) A spring regime, associated with the destabilization of the snow cover with temperatures above 0 °C and liquid precipitation such as the rain-on-snow-event reported on May 4th 2011 (Table 1). The role of rainfall in triggering snow avalanches has also been reported elsewhere several times (Butler, 1986; Conway, 2004; Conway and Raymond, 1993; Marks et al., 1998; McClung and Schaerer, 2006; Ward, 1984). However, considering the low number of events characterized by rainfall during the 72 h preceding the event, it is difficult to Table 3 Statistics about the number of days with a minimum temperature above 0 °C and quantity of rainfall (mm) on a monthly basis for the period 2000–2011. Tmin N 0 °C

Mean SD Max Min

Rainfall (mm)

Dec

Jan

Feb

March

Dec

Jan

Feb

March

8.1 4.4 16 1

5.3 3.7 10 2

3.5 6.8 12 0

10.9 7.1 22 1

7.5 5.0 13.9 2.0

4.8 3.6 11 1.2

7.2 3.5 10 3.0

4.9 4.5 15.6 1.0

Note that the month of April is not shown because most of the days are characterized by a minimum temperature above the freezing point.

88


a

b

c

d

Fig. 7. Aqua Velva ice-structure and path studied on March 31st before (a) and after (b) a wet snow avalanche triggered by an ice-block fall. c) Close-up of the ice wall after the avalanche (see the difference by comparison to a). d) Late activity of ice-block falls which impacted the small retaining wall along the national road 132. Source: courtesy of B. Hétu (cf. Hétu, 2007).

precisely define the role of rainfall as a trigger for snow avalanches at this study site. As shown in Table 3, warm air temperatures (Tmin N 0 °C) and liquid precipitations occur more often in early winter (December) and spring (March) but can also happen in the middle of winter (January and February).

Relative frequency of occurrence (%)

100 90 80

Phase 1: April 21- May 6 Relative frequency 53.3%

70 60

5.2. Potential synergistic effect of hillslope processes

50 40

Phase 2: May 6 - 19 Relative frequency 23.3%

30 20

Phase 3: May 19 - June 5 Relative frequency 23.3%

10 0

April 1

May 1

June 1

July 1

Fig. 8. Timing and relative frequency ice-block falls occurrence and sufficient snow cover (≥50 cm) for avalanching for the period 1987–2011. The dark gray boxes are delimited by the average time period corresponding to the limits of the three phases of ice-block fall activity based on DD (phase 1: 35–100 DD; phase 2: 100–185 DD; and phase 3: 185–345 DD). Light gray boxes represent the standard deviation. The black line represents the average snow cover thickness estimated.

Although the influence of ice-block falls on snow avalanche release has already been mentioned by Hétu (2007) and Gauthier et al. (2012), with the presence of ice-blocks in avalanche deposits, the consideration of ice-block falls as a snow avalanche trigger remains poorly documented. Indeed, the narrow snow avalanche paths facing north and characterized by an ice wall in the upper part of the slope experience a thick snowpack, which usually persists for a long period in comparison to the large scree slopes of the northern Gaspésie due to the absence of a strong wind effect (Fortin et al., 2011). In this respect, the avalanche hazard in these narrow paths persists for a longer period of time considering the duration of snowpack but also the ice-block fall dynamics. As illustrated in Fig. 8, the snowpack appears sufficient


(N50 cm) for snow avalanche activity with a decreasing probability during the three phases of ice-blocks fall activity. Although these data are based on a small dataset of 13 and 5 years of ice-blocks fall and snow avalanche activity, respectively, these results show that these both processes can interact and promote the occurrence of avalanches following ice-blocks falls. In this respect, the important variability of weather conditions during the spring season in this maritime environment, which results in an unstable snow cover, is certainly a precursor or facilitating factor for avalanche occurrence. Although extreme snowfall may occur in April, rainfall and significant warming temperatures have been more frequently recorded at that time period; the latter conditions favoring the melting and collapsing of ice structures (Gauthier et al., 2015b). Nevertheless, the calculation of reel conditional probability of snow avalanche triggered by an ice-block fall requests more information about the characteristics of the snowpack (thickness, structure and stability) but also the collapse of the ice-structure (size of the blocks, intra-annual frequency, etc.). Indeed, the modeling of Rango and Martinec (1995) we applied, even with the use of a high density of snow, seems to overestimate the thickness of the snow cover in comparison to the field observations. 6. Conclusion Road safety in the northern Gaspésie depends intrinsically on our ability to understand the dynamics and triggering conditions of hillslope processes that occur on the steep slopes along the St. Lawrence River. Indeed, this knowledge is fundamental to the management of hazards and risks (i.e. the reduction of vulnerability) and to the implementation of adapted and efficient mitigation measures (Hétu et al., 2011). In this respect, long-term monitoring of these narrows paths should be implemented considering that snow avalanches and ice-block falls may occur, and this particularly since the end of March until the month of June where both processes are likely to interact. In the province of Quebec, a multi-risk approach is widely encouraged and desirable. However, considering the current lack of information and limited understanding of geomorphic processes, future research is needed for efficient long-term, multi-risk management. The cumulative degree day is reported here and by Gauthier et al. (2015b, 2012) to be a good indicator of ice-block fall hazards. Unfortunately, glaciological, hydrological and geomorphological data are still needed to better understand the collapse of ice walls (cf. Gauthier et al., 2015b) and snow avalanche dynamics as well as the implementation of additional weather stations along the coast and a program to monitor the physical properties of the snow cover (thickness, density, etc.). However, the characterization of snow avalanche regimes and their occurrence timing in the Aqua Velva corridor is expected to assist the MTQ in improving its management of hillslope hazards along national road 132 in the northern Gaspésie.

Acknowledgment This work was supported by a MSc to Marie-Hélène Graveline from the Fonds Québécois de Recherche Nature et Technologies (FQRNT). The authors would like to thank Thibault Roquefort and Jean-Philippe Martin for their help in the field. The comments of two anonymous reviewers were particularly helpful to improve the quality of the paper.

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