Quantifying road vulnerability to coastal hazards: Development of a synthetic index

Ocean and Coastal Management 181 (2019) 104894

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Quantifying road vulnerability to coastal hazards: Development of a synthetic index

T

Susan Drejzaa,b,*, Pascal Bernatcheza,b,c, Guillaume Mariea,d, Stéphanie Friesingera,b a

Chaire de Recherche en Géoscience Côtière, Université du Québec à Rimouski, 300 allée des Ursulines, G5L 3A1, Rimouski, Qc, Canada Centre for Northern Studies (CEN), Université du Québec à Rimouski, 300 allée des Ursulines, G5L 3A1, Rimouski, Qc, Canada c Québec-Océan, Pavillon Alexandre-Vachon, 1045, Avenue de la Médecine, local 2078, Université Laval, Québec, Québec, G1V 0A6, Canada d Laboratoire d'archéologie et de patrimoine, Université du Québec à Rimouski, 300 allée des Ursulines, G5L 3A1, Rimouski, Qc, Canada b

ARTICLE INFO

ABSTRACT

Keywords: Coastal erosion Coastal flooding Vulnerability index Transport network Quebec Canada

As part of a collaborative study with the Ministry of Transport of Québec, a Coastal Road Erosion and Flooding Vulnerability Index (CREFVI) was developed for the short (2020), medium (2060) and long term (2100). Nine study sites in Eastern Québec (Canada) were used to develop the index, for a total of 122.4 km of roads. The index includes 14 parameters relating to the following: the exposure of a site to erosion and coastal flooding hazard; the characteristics of the road segment; the characteristic of the road network; and adaptation to erosion (e.g. the presence of defence structures). Each parameter received a score between 1 and 5 according to its propensity to increase (5) or not (1) the vulnerability of the road. CREFVI values for the study sites vary between 0 and 159.1 and have been divided into 5 classes reflecting the actions needed to reduce the site's vulnerability: Not vulnerable (no intervention necessary), Low, Medium, High, Critical (immediate intervention required). For the roads analyzed, 0.5 km have a critical CREFVI value as of 2020, 1.7 km as of 2060 and 2.6 km by 2100. Currently, only 20.6% of the road segments are vulnerable to coastal hazards, but this percentage rises to 36.4% by 2060 and to 54.3% by 2100. This index is an important decision-making tool for improving road management, prioritizing future actions and determining which parameters require intervention.

1. Introduction Vulnerability can be defined in many, often divergent, ways depending on the researcher and field of research (D'Ercole, 1994; Adger, 2006; Paul, 2013; Nguyen et al., 2016). For some authors the concept is a potential exposure to a threat (Gabor and Griffith, 1980), for others it is a degree of loss associated with a hazardous event (Cutter, 1996). Vulnerability can also be defined as an inability to face the adverse effects of a hazard (McCarthy et al., 2001) or as the propensity to experience damage or harmful effects (Wolf et al., 2013). Therefore, clarification is necessary not only for our definition of vulnerability for this study, but also for the different concepts that are referred to. We define infrastructure's exposure to a hazard as its potential to be affected by said hazard (Füssel and Klein, 2006), sensitivity of a coast as the degree to which it is modified or affected by perturbations (McCarthy et al., 2001) and adaptive capacity as all existing structural and organizational measures minimizing the foreseeable consequences of one or more hazards, as conceptualized by Smit and Wandel (2006).

Vulnerability is defined in this case as the propensity or predisposition to be adversely affected (IPCC, 2014). Therefore, vulnerability of structures, in our study, depends on the degree of exposure as well as the sensitivity of the system to hazards (or disturbances), the stakes involved, and the adaptive capacity employed to reduce or eliminate the adverse effects of hazards on the system (Adger, 2006). The concept of systemic vulnerability, which is more inclusive, and which aims to estimate the fragility of a system as a whole, also takes into account the risk perception of residents, managers and other stakeholder (D'Ercole, 1994; Meur-Férec et al., 2008). Vulnerability assessment often requires calculating indices. This type of index measures a number of factors and is not limited to the degree of exposure (Paul, 2013) — an index makes it possible to predict and compare different sites using objective criteria with a view to improving management (Sterr, 2008; Johnston et al., 2014) and to prioritizing vulnerable regions or sectors (Nguyen et al., 2016). Over the last two decades, many studies have examined the vulnerability of coastal populations and infrastructure to rising sea levels

*

Corresponding author. Chaire de recherche en géoscience côtière, Université du Québec à Rimouski, 300 allée des Ursulines, G5L 3A1, Rimouski, Qc, Canada. E-mail addresses: [email protected] (S. Drejza), [email protected] (P. Bernatchez), [email protected] (G. Marie), [email protected] (S. Friesinger). https://doi.org/10.1016/j.ocecoaman.2019.104894 Received 15 April 2019; Received in revised form 10 July 2019; Accepted 19 July 2019 Available online 17 August 2019 0964-5691/ © 2019 Elsevier Ltd. All rights reserved.

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and storms in the context of climate change (Klein and Nicholls, 1999; McLaughlin et al., 2002; Dolan and Walker, 2006; Pendleton et al., 2004; Boruff et al., 2005; Colle et al., 2008; Harvey and Nicholls, 2008; Sterr, 2008; Balica et al., 2012; Arkema et al., 2013; Gorokhovich et al., 2014; Johnston et al., 2014; Bagdanaviciute et al., 2015; Bonetti and Woodroffe, 2017; Cogswell et al., 2018) and, more recently, the exposure of populations at the global scale (Muis et al., 2017). These studies are even more pertinent today given the accelerated rate of sea level rise over the last decade (Merrifield et al., 2009; Church and White, 2011; Rahmstorf et al., 2012), the latest projections of the IPCC (2013) estimating a sea level rise of 0.28–0.98 m by 2100 (Church et al., 2013) and the even higher estimates by other researchers ranging from 1.2 to 1.5 m (Pfeffer et al., 2008; Grinsted et al., 2009; Vermeer and Rahmstorf, 2009; Nicholls and Cazenave, 2010; Nicholls et al., 2011; Parris et al., 2012; Horton et al., 2014). Current scientific knowledge predicts a surge in certain hazards in the short and medium terms, as well as an increase in their intensity (Lozano et al., 2004; Zhang et al., 2004; IPCC, 2013). These environmental changes affect the rate of erosion and the frequency of flooding in low-lying coastal areas. In cold coastal regions like Québec, it is also important to take into account the reduction in coastal ice (Senneville et al., 2014), which leads to greater wave impact over a longer period of time during the year. In Eastern Québec (Canada), nearly 50% of the coastline is undergoing erosion processes (if coasts composed of igneous and metamorphic rocks are excluded) (Drejza et al., 2014) and 49% consists of low-lying coasts potentially at risk of flooding. Nevertheless, historical settlements, human occupation and communication infrastructure are all concentrated along the coast in these regions. Moreover, nearly 60% of the provincial roads are less than 500 m from the coast. Erosion and flooding have already led to problems for some provincial roads (e.g. traffic interruptions, urgent repairs…), so 274 road segments are currently monitored by the Ministry of Transport of Québec (MTQ) for erosion and coastal flooding issues. It is in this context that the MTQ expressed the need to be better equipped with tools to manage coastal roads. In addition, the need to quantify vulnerability was also raised. The aim of this research, developed in collaboration with the Ministry, was to develop an objective and functional method to assess the vulnerability of roads to erosion and flooding. Numerous studies on vulnerability have undeniable scientific interest but do not respond to the practical needs of the management agencies responsible for implementing adaptation solutions that reduce vulnerability, thereby limiting the usefulness of such studies. We present an approach developed specifically for road vulnerability in the form of an index, along with the main results of our study.

updatable tool for managers in the transport sector and other professionals, as recommended by Hénaff and Philippe (2014). The proposed synthetic index is composed of two sub-indices, one for the erosion hazard and the other for flooding, which are combined to produce the global index (CREFVI).

2. Study sites

3.2. Parameters

Nine study sites were selected to develop the methodology and the vulnerability index. The sites are representative of the coastal environments of the maritime estuary and gulf of St. Lawrence (Québec, Canada) and are all at risk from coastal erosion and flooding. They were selected jointly with the central and regional branches of the MTQ based on their location in areas already identified as problematic and using data on road exposure from the study by Drejza et al., 2014. The sites (Baie-des-Sables to Saint-Ulric, Rivière-à-Claude, Chandler, Maria and Carleton, Baie de Plaisance, Pointe-aux-Loups, Pentecôte, Rivière au Bouleau and Rivière-Saint-Jean to Longue-Pointe-de-Mingan) represent 122.2 km of provincial roads divided into 124 segments, mostly 100 m in length (Fig. 1).

The quantification of vulnerability was preceded by a survey of all potentially influential parameters. Following this, a selection was made of easily quantifiable but mutually non-redundant elements. Decisions were based on scientific literature and knowledge garnered during consultations with managers of the MTQ and other professionals. A list of 14 parameters was compiled (Table 1), some of which combine several measurable elements. Each parameter is assigned a score between 1 and 5 depending on its propensity to increase (5) or not (1) the vulnerability of the road. The intervals for the scores are relatives and are based on literature and discussion with other researcher and managers. This type of scoring system is also used by many other authors, such as Gornitz (1991); McLaughlin et al. (2002); Boruff et al. (2005); Meur-Férec et al. (2008); Fontaine and Steinemann (2009); McLaughlin and Cooper (2010) and Cogswell et al. (2018). Each of the two subindices (erosion; flooding) is calculated using 10 of the selected parameters (Table 1). So, six parameters are used twice in the global index, they have a strong importance thereby they have more weight in the global result.

3.1. General principles The key objectives of the index are to promote proactive asset management and thus prevent damage to the road network, therefore three time horizons were retained: short (2020), medium (2060) and long term (2100). CREFVI 2020 considers the current state of flooding and the coastal erosion that would take place from now until 2020. This index focuses on urgent management for more vulnerable segments. It can be thought of as short-term vulnerability. CREFVI 2060 takes into account the flooding in 2060 caused by local isostatic adjustments in the Earth's crust, significant in Québec, and rising global sea levels, as well as the erosion that would take place from now until 2060. The focus is on preventive management of the road network and intervention planning. It can be thought of as medium-term vulnerability. Finally, CREFVI 2100 represents the vulnerability to coastal hazards from now until 2100 and takes into consideration the flooding in 2100 caused by sea level rise and isostatic adjustments, as well as the erosion that would take place from now until 2100. This scenario considers a more distant timeframe and thus involves greater uncertainty, but it does allow for long-term planning. It can be thought of as long-term vulnerability. Roads were digitized at 1:600 scale using the road side line on the most recent aerial orthophotos (2007–2012, 15–20 cm resolution), and the vulnerability analysis was performed on 100-m segments. The coastline was also digitized on the same image using the limit of dense vegetation for low-lying coasts, the tops of the escarpment for cliff coasts and the tops of defence structures for built coasts. The shortest distance between the coast and the road segment was measured automatically using the Near Analysis tool in ArcGIS. The elevation used was the lowest elevation along the road segment using LiDAR data (except for one study site where it was necessary to use a DEM generated from topographic contours). Bridges and their approaches were not considered in this study. Bridges pose distinctive issues (different coastal dynamics and the presence of fluvial erosion) and cannot be addressed in the same manner as other road segments. Their vulnerability is better evaluated by specific studies as recommended by the U.S. Federal Highway Administration (2008) and Kafalenos et al. (2008).

3. Methodology The Coastal Road Erosion and Flooding Vulnerability Index (CREFVI) must contain sufficient criteria to reflect the complexity of the natural and anthropogenic system while ensuring adequate reproducibility of the method, such that it remains a workable and easily 2

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Fig. 1. Location of the study sites used to develop the synthetic index.

CREFVI considerations:

(number of rows of infrastructure between the road and the coast), presence of ancillary infrastructure along the road (electricity, water supply, sewers, etc.), ease of re-establishing services and driver safety in relation to erosion or flooding. - The characteristics of the network, such as the presence or absence of a bypass route and its length, as well as access to essential services (fire station and hospital) (measured by parameters 12–14 in

- Exposure to erosion and flooding hazards (measured by parameters 1–4 in Table 1). - The characteristics of the road segment measured by parameters 5–11 (Table 1): number of vehicles circulating each day (AADT), population residing directly along the 100-m segment, coastal row Table 1 Parameters used to calculate CREFVI. N°

Parameter

Erosion sub-index

1

Year of road's exposure to erosion - Distance between road and coast (in metres) - Retreat event (2.2–16.9 m depending on coastal type and study site. 0 m for igneous and metamorphic rock coasts) - Most likely migration rate (in m/yr) (rate is 0 m/yr if defence structures of the MTQ are present and in good condition) Possible water depth on road - Altitude of road - Possible flood height above higher high water large tide - Local impact of defence structures on flood height - Sea level rise (for 2060 and 2100) - Isostatic adjustments (for 2060 and 2100) Beach width Distance between road and coast (attenuation of water body and waves) Annual Average Daily Traffic (measured by the MTQ) Population residing directly along the road segment (based on the number of buildings and the average number of people per household in the municipality) Propensity for simple road repairs following a road rupture caused by erosion (depth to backfill, instability of the ground, etc.) Coastal row (relative position in relation to the coast; number of infrastructure rows between the road and the coast) Presence of ancillary infrastructure at risk of being damaged (aqueduct, sewers, electricity, etc.) Driver safety in relation to erosion (suddenness of the erosion process, depth of the gap left by erosion, etc.) Driver safety in relation to flooding (violent surge) Presence of a bypass route (if yes, length of the bypass) Access to fire station - Affected population - Length of the detour (if a detour is possible) Access to a hospital - Affected population - Length of the detour (if a detour is possible)

X

2

3 4 5 6 7 8 9 10 11 12 13 14

3

Flooding sub-index

X

X X

X X X X

X X X X

X

X X

X X X

X

X

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Fig. 2. Concrete sea wall defence structures along road 132, partially destroyed by the storm of December 6, 2010; Haute-Gaspésie, Québec, Canada (© MTQ).

Table 1). This is a measure of the redundancy of the roadway segment. - Adaptation to erosion is taken into account via the impact of defence structures (if present) on the erosion rates. No adaptation for flooding is present at our study sites, but if applicable, it would be incorporated into the index by following the same principle; that is, by adjusting the exposure to flooding (water depth) to reflect the protection offered by existing structures.

transportation and climate change adaptation were not taken into consideration as they affect all of Québec the same way. 3.3. Details of the parameters Details of the scores assigned to the parameters used to calculate CREFVI are presented in Tables 2 and 3. The types of parameters are general and may be used elsewhere, but the scores reflect the reality of the study sites (Eastern Québec) and would need to be adjusted if CREFVI was applied to other regions or countries. It is important to consider that vulnerability is specific to the context (O'Brien et al., 2007). The parameters used for this study were presented to the Ministry and validated because the involvement of road network managers is essential if the results are to provide tangible assistance in management and prevention (Hinkel, 2011; Johnston et al., 2014). After discussions with road managers and because of the available data, the two parameters for driver safety (n°10 and 11) were only divided into 3 categories instead of 5. For hazard exposure, we prepared a description and detailed analysis of the coastal dynamics and processes at each study site, as well as its historical coastal evolution, in order to characterize as precisely as possible the erosion and flooding hazards that could affect the road infrastructure in the area. Erosion exposure is calculated as the distance between the road and the coast from which the maximum retreat event is subtracted (the maximum erosion recorded in a single episode during the past; varies depending on type of coast and the region), all divided by the most likely annual migration rate (Drejza et al., 2014; Fraser et al., 2017). This gives the number of years before the road is affected

The impact of vulnerability adaptation was factored into the index and differs depending on the type of measure. Structural adaptation measures, such as the armoring of a coast with rigid structures, have been included by adjusting the most likely migration rate for roads situated behind these structures. As such, if rigid structures are present and in good apparent condition based on field visit, and if they were built by the MTQ, the long-term erosion rate was considered nil. However, major storm event can lead to a breach in the coastal defences, as evidenced by the many examples of destroyed structures (Fig. 2). Should this occur, the Ministry would reconstruct such defence structures in the same place, resulting in no long-term erosion rate. Non-structural adaptation measures, such as alternative modes of transport and surveillance patrols, were not taken into account in a quantitative manner. This decision stems from a preference to quantify the “real” vulnerability of a site, and then to present the existing options and discuss which measures should be taken and/or implemented. Moreover, these measures are not necessarily implemented to specifically reduce coastal hazard vulnerability and may be less sustainable than rigid infrastructure. Finally, provincial politics regarding 4

5

Coastal row

Ancillary network

Driver safety in relation to erosional processes

Driver safety in relation to wave breaks

Bypass road

8

9

10

11

12

7

6

3 4 5

Exposure to flooding (based on projected water levels for 2020, 2060 and 2100 according to scenario RCP8.5 of the IPCC and the isostatic adjustments of Koohzare et al., 2008) Beach width Distance between road and coast Annual Average Daily Traffic (number of vehicles) Population residing directly along road segment Ease of repairing a road rupture

2

CREFVI 2100

CREFVI 2060

Year of road's exposure to erosion

1

CREFVI 2020

Parameter

N°

Table 2 Details of the scores used to calculate CREFVI.

n/a

n/a

n/a

n/a

n/a

n/a

n/a

n/a n/a n/a

exposed after 2020 exposed after 2060 exposed after 2100 no water

0 - nil

score of 1: < 15 km score of 1.5: 15–29.99 km

other situations

No difficulties: low-lying coast – relatively easy road to repair Row 5 or + (4 rows or more of infrastructure between road and coast) no network or only rainwater drainage low-lying coasts, igneous or metamorphic rock coasts

less than 2 people

30 m and more 100 m and more less than 1000

30–49.99 km

n/a

n/a

No difficulties: rocky or sandy cliffs, flat tops, no risk of landslides Row 4 (3 rows of infrastructure between road and coast) 1 network

2 to 4,99

20–29.99 m 50–99.99 m 1,000 to 1,999

exposed between 2060 and 2100 0–0.2 m of water on the road

n/a within the margin of error of Lidar data (0.2 m)

n/a

n/a

2 - low

n/a

n/a

1 – very low

coast composed of sedimentary rock cliffs, coast composed of sandy cliffs steep slope from coastline overtopping of shoreline: flooding + proximity (< 10 m) + coast without defence structure + “row 1″ 50–99.99 km

Row 3 (2 rows of infrastructure between road and coast; the road is the 3rd row) 2 networks

Some difficulties: lagoon, water course, pond, land with significant relief, etc.

5 to 6,99

10–19.99 m 20–49.99 m 2,000 to 3,999

0.2–0.5 m of water on the road or breach of the defence structure

exposed between 2040 and 2060

exposed between 2040 and 2060

n/a

3 - medium

more than 100 km

n/a

n/a

Multiple difficulties: for example, significant relief + water course Row 2 (1 row of infrastructure between road and coast) 3 networks

7 to 9,99

less than 10 m 10–19.99 m 4,000 to 5,999

exposed between 2020 and 2040 exposed between 2020 and 2040 0.5–1 m of water on the road

exposed by 2020

4 - high

none

overtopping of defence structure: flooding + proximity (< 10 m) + “row 1” + coast with defence structure

coast composed of unconsolidated cliffs with potential landslides

Row 1 (no rows of infrastructure between road and coast; the road is the 1st row) 4 or 5 networks

Very difficult: cliffs subject to landslides, unstable soils

10 and more

no beach less than 10 m 6,000 and more

exposed imminently from now until 2020 exposed imminently from now until 2020 more than 1 m of water on the road

exposed imminently

5 – very high

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Table 3 Details of the scores for the parameter “Access to a hospital/fire station” (n°13–14). Number of people affected

less than 25 25 to 100 100 to 500 500 to 1000 more than 1000

Length of detour < 15 km

15–29.99 km

30–49.99 km

50–99.99 km

> 100 km

no detour possible

1 2 2 2 2

2 2 3 4 5

3 3 3 4 5

4 4 4 4 5

5 5 5 5 5

5 5 5 5 5

by erosion. The most likely annual migration rate is the rate during the most recent period of coastal evolution obtained by photo-interpretation (between 7 and 19 years depending on the location and availability of images). If the road is already in an area that could be reached by a retreat event, the risk is considered imminent. Exposure to flooding is the elevation of the road compare to the potential flood level. The flood level is the highest value reached on the site during a storm where such measurements exist. When not available, a regional flooding level is used and added to the higher high water large tide value. The regional flooding level is derived from the difference between the highest value measured in the area (regional scale) during a storm and the higher high water large tide as measured at the nearest station. The flooding level takes into account the effect of rigid defence structures to cause flooding up to 1 m above what is measured in adjacent natural areas (Bernatchez et al., 2011; Didier et al., 2016). The average sea level rise and isostatic adjustment values that are added to the future vulnerability indexes (2060 and 2100) are variable and determined by the region in Québec. The isostatic adjustment is derived from the model of Koohzare et al. (2008), except for Magdalen Islands where the data are not precise enough and measurements from Juneau (2012) were used. The median values of the IPCC scenario RCP 8.5 (pessimistic scenario with rising emissions during the 21st century), were taken for the sea level rise projections in response to climate change: 0.30 m for 2060 and 0.74 m for 2100 (IPCC, 2013). This scenario is often used for vulnerability index (Cogswell et al., 2018). “Coastal row” quantifies the rows of infrastructure between the road and the coast. From a flooding point of view, these limit the propagation of water and, above all, waves. From an erosion point of view, these features can limit sudden erosion during a storm by constituting obstacles to coastal processes. This parameter influences vulnerability because the farther the road is situated behind other structures, the more it is “protected” by them. This is why the scores are inversely proportional to the coastal row numbers (Table 2). It is not a pretext to build more structures on the coast to “protect” the road, but to reflect the actual conditions on the coast. It also seems equally important to consider residences served by the road, as well as the people who would become stranded or who would need to take a detour in the event of a disaster. These factors were also included in the index developed by Johnston et al. (2014) for public coastal infrastructure (including roads). For the bypass road score, some places may not have one (over land). Historically, the settlement dynamics in Eastern Québec were entirely concentrated along the coastal fringe. To this day, settlements remain very linear in Eastern Québec. The unpaved bypass roads at certain localities (logging roads, for example) are not considered in this study as they may not be practical for all types of vehicles or in all seasons (e.g., impassable in winter).

methodology allows the vulnerability to be annulled if the exposure is nil (because the score is 0 for the factor) and prevents certain parameters from increasing vulnerability (if their score is 1). This type of calculation is used by many authors to develop sensitivity or vulnerability indices, such as Gornitz (1991), Pendleton et al. (2004), Boruff et al. (2005), Gorokhovich et al. (2014), Bagdanaviciute et al. (2015) and Cogswell et al. (2018). No weighting was applied to the different parameters. Equation (1). Sub-index equation (erosion or flooding)

Sub

index =

([parameter 1]*[parameter 2]* […]*[parameter N ]) / N

Both hazards (erosion and flooding) are considered to have similar weights. Thus, CREFVI is the average of the two indices (Equation (2)). Equation (2). CREFVI

CREFVI = ([Erosion Vulnerability] + [Flooding Vulnerability])/2 3.5. Classification The results were divided into 5 classes based on the level of action needed to reduce the vulnerability of road segment to coastal hazards (Table 4). Thresholds were established empirically as a function of the problematic realities of certain sites. 4. Results 4.1. Degree of vulnerability of the study sites For the nine study sites, CREFVI values range from 0 to 159.1. Five road segments have a critical CREFVI by 2020, 17 segments by 2060 and 26 segments by 2100 (Fig. 3). While only 20.6% of the road segments are currently vulnerable to coastal hazards, this proportion will rise to 36.4% by 2060 and to 54.3% by 2100. Mapping is essential to establishing a good spatial representation of the situation and to better communicating the results (Preston et al., 2011; Bonetti and Woodroffe, 2017). The vulnerability of each study site was mapped at different time horizons for the MTQ, which allows sites to be compared and most vulnerable segments of road to be identified. As an example, Fig. 4 presents the CREFVI values for all nine study sites for the 2100 horizon. This synthetic index was mapped for the different time scales (2020, Table 4 Degree of vulnerability based on the actions to be taken. Rank

Action to be taken

Class

Nil (not vulnerable) Low

no intervention necessary long-term intervention planning, monitoring will be needed medium-term intervention planning or on a case-by-case basis rapid intervention necessary immediate intervention necessary

0 0 < CREFVI < 10

3.4. Calculation of CREFVI

Medium

The parameters in the vulnerability calculation are multiplied together and divided by their number (in this case, 10), and the sub-index is the square root of the calculation (Equation (1)). This type of

High Critical

6

10 ≤ CREFVI < 25 25 ≤ CREFVI < 50 equal to or above 50

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Fig. 3. Distribution of road segments by vulnerability rank and reference period.

2060, 2100) for each study site, as well as the two sub-indices (erosion and flooding). Fig. 5 presents the example of the Baie de Plaisance site in the Magdalen Islands. The vulnerability rank corresponds to the time horizon at which action must be taken. If the degree of vulnerability is critical (CREFVI ≥ 50), attenuation actions and/or adaptation measures should be implemented immediately. If the degree of vulnerability is high (CREFVI of 25–50), solutions for this segment should be quickly evaluated. The lower the vulnerability degree, the less urgently the solutions need to be applied. However, solutions should be envisioned starting now, even for less vulnerable sites, so that adaptation measures can be implemented at lower cost during the next road network upgrades (Johnston et al., 2014). It is important to keep in mind that regardless of the degree of vulnerability, the exposure to hazards can be significant even in cases where the level is low (less than 10). Finally, if the vulnerability rank is nil (a score of 0), hazard planning is not

necessary. However, the situation should be considered in the context of the network. For example, a non-vulnerable segment between two vulnerable segments must be included in the overall planning. 4.2. Elements that increase vulnerability The analysis of all nine study sites at the three different time horizons revealed four elements that contribute the most to increasing road vulnerability in addition to its physical exposure to hazards. They are: (1) high average daily traffic, (2) no bypass road, (3) service interruptions for residents or severed access to emergency services (fire station and/or hospital), and (4) a road situated in an area where cliffs have experienced landslides resulting in a dangerous situation for drivers and difficult conditions for repairs due to unstable ground.

Fig. 4. Vulnerability index for the nine study sites. 7

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Fig. 5. Sub-indices and global index for the Baie de Plaisance site (2020, 2060 and 2100).

4.3. Elements that limit vulnerability

5. Discussions

On the other hand, some elements reduce the vulnerability of a road segment. They are: (1) the presence of a bypass road (robust network or redundancy), (2) implemented hazard adaptation measures and (3) maintained defence structures. However, in several places where defence structures are present and the erosion sub-index is thus low or nil, the risk of flooding is higher (less voluminous beaches, higher flood levels) and so is the danger (more violent wave breaks), resulting in overall road vulnerability to coastal hazards (Bernatchez et al., 2011; Bernatchez and Fraser, 2012).

5.1. Originality of the method 5.1.1. A predictive and evolutionary tool CREFVI is a predictive tool. Although field managers often have good knowledge of segments with imminent exposure, their information on segments exposed in the medium to long term is very limited. Yet roads are infrastructure intended for long-term use and decisions must reflect this fact. A lack of long-term vision or knowledge of the issues will often lead to reactive management and the implementation of piecemeal solutions that could have a detrimental effect in terms of erosion (Williams et al., 2018; Pranzini et al., 2015) or could be less effective but more costly than well-planned solutions (London, 2018). The results of the vulnerability analysis can be used to identify and implement preventive adaptations to hazards during regular maintenance work, as suggested by Johnston et al. (2014). One such example would be raising the road during routine repaving, resulting in the implementation of an adaptation measure for only a small additional investment (Johnston et al., 2014).

4.4. Use of the results CREFVI synthesizes several parameters into a single index. For this reason, the vulnerability index should serve as a tool in decision-making and prioritizing actions. Nevertheless, it is also important to recognize the erosion and flooding sub-indexes to better understand the situation of the road segment and envision management strategies and alternative scenarios. The data are provided in digital format to MTQ managers (Drejza et al., 2015). Once the most vulnerable segments and the main hazard to which they are exposed are identified, the focus should be on the parameter scores that determine the CREFVI value in order to decide which actions would reduce road vulnerability. For the same degree of vulnerability, several factors may be at play. Thus, the appropriate actions are not necessarily the same. Moreover, as CREFVI combines two coastal hazards that may interact with each other, the resulting vulnerability class incorporates such interaction. Erosion and flooding management should be conducted in an integrated manner (Dawson et al., 2009).

5.1.2. Local scale Barnett et al. (2008) and Hinkel (2011) noted that approaches based on indicators like those in the present study are only appropriate at local scales for which the systems can be narrowly defined, using only a few variables (Nguyen et al., 2016). However, precise measurements taken locally at the scale of 100 m coupled with the fairly homogenous human and natural conditions of Eastern Québec make it appropriate to apply our method even though the regions for which CREFVI was developed cover more than 3,000 km of coast. This allows appropriate adaptation measures to be planned at the local scale (Sterr, 2008). The 8

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scale also allows managers to identify the most fragile segments of the transport network (Johnston et al., 2014). Because vulnerability is very variable and is related to specific geographic location (Weichselgartner, 2001; Paul, 2013), a study using small segments can adequately reveal spatial variations even on such a large territory.

Our method does not consider other hazards that may influence overall road vulnerability, such as movements of clay-rich soil, landslides, avalanches, ice falls, rockslides, etc. Some of the studied roads could thus be much more at risk than indicated by the CREFVI index. Also, the method does not take the domino effect into account, i.e., the series of consequences that may result from a road rupture or certain sections becoming isolated due to the rupture of multiple road segments. A specific analysis such as that presented by Robert and Morabito (2011) could be considered in certain areas. Finally, the index does not take financial factors into consideration (neither the cost of the rupture on the economy nor the cost of repairs or adaptation measures to be put in place). The authors are aware of the limitations of the proposed methodology. However, with the current knowledge and available data in our region, our approach is the most complete and accurate possible.

5.1.3. Complementary tool for exposure analysis By using areas identified as exposed to hazards in a previous study (Drejza et al., 2014), the vulnerability assessment of this study was able to focus solely on areas likely to be affected by erosion or flooding. Other studies have also used this approach, which minimizes the cost and time needed to conduct the vulnerability study (Johnston et al., 2014). 5.1.4. Selected variables We opted for a limited number of criteria to keep the proposed index functional and relatively easy to use (Nguyen et al., 2016). This also facilitates updates, which are essential if environmental changes or new scientific data are to be incorporated (Hinkel, 2011; Hénaff and Philippe, 2014). We consider the selected criteria to be an acceptable compromise between the complexity of the system and the time needed to collect, process and update the data. For example, using the annual average daily traffic to quantify the importance of the road was also used by Johnston et al. (2014) in Maine. Even if the number of vehicles seems low, such as in the study by Johnston et al. (2014), the small population of the study area must be kept in mind (i.e., less than 300,000 inhabitants for Eastern Québec, Institut de la statistique du Québec, 2017). It is in this context that the number of people living directly on the 100-m road segments should be considered (see Table 3); the number may seem small but it reflects the reality of the region.

5.3. Research partnerships In preliminary meetings with the MTQ, we found that people within the same department had different definitions of vulnerability. For some, the notion of vulnerability was more synonymous with exposure. Some managers said that because they were very familiar with their territory, they only required the data on the exposure of roads to hazards. From this data they could then evaluate the responses and prioritize actions themselves. This type of reaction has also been noted by Schröter et al. (2005). However, the CREFVI that has been developed integrates quantitative parameters on road vulnerability objectively. Moreover, this index makes it possible to work across Eastern Quebec, comparing sites, even if they are not in the same manager's territory. In fact, the inventories and measures that exist in the different areas are not homogeneous and it is not always possible to make comparisons. A regional department of the Ministry has integrated the use of several elements developed for CREFVI in their annual follow-ups to refine and improve their evaluation of the problem, the decisionmaking and the updating of the index (Y Blanchard, pers. comm. 2015). The database was built in a software that is well known by the MTQ (ArcGIS) and given to them and was thought to be easy to update (new data and new calculation of the CREFVI index) (Drejza et al., 2015). To ensure the accuracy of the index, it is important to conduct regular and post-event monitoring of the entire road network, and regularly update the distance between the road and the coast. This allows easier collaboration with the Ministry, which is in progress.

5.1.5. Synthetic index and dissociation into sub-indices to prioritize actions A synthetic index can be broken down into sub-indexes so that adequate solutions may be implemented based on the particular element responsible for increasing the vulnerability of a given area. Thus, it is possible to determine if the parameter in question is related to the road, the network, the exposure to flooding or erosion, or a combination of several parameters. This provides the advantage of a single quantification, but information obtained from all the parameters and sub-indexes also make it possible to prioritize actions based on hazard exposure, hazard adaptation and the local characteristics of the road segment and network. The index thus sheds light on which road segments to prioritize and which actions to take. For example, such actions could include raising the road, moving ancillary networks, creating bypass roads (to increase the robustness/redundancy of the network), elaborating an alternative transportation plan, and strengthening existing local adaptation plans in case of emergency. This is why a detailed analysis was completed for each site and given to the managers along with the database and mapping results.

6. Conclusion This study led to the development of the Coastal Road Erosion and Flooding Vulnerability Index (CREFVI) that considers erosion and coastal flooding. Vulnerability indices calculated for 100-m segments across nine study sites (totalling 1,224 segments) can be divided into 5 classes of vulnerability: Nil, Low, Medium, High and Critical. Despite the method's limitations, this index, as a planning and management tool, is important for prioritizing future actions and determining on which parameters to intervene. Therefore, a vulnerability analysis is a good tool for supporting decisions, prioritizing issues and prioritizing actions in the short, medium and long term. This tool favours anticipation rather than reaction, which is preferable in the process of adapting to climate change (Klein et al., 2001), and it allows, where possible, hazard adaptation to be integrated into routine maintenance to finally reduce costs (Johnston et al., 2014). In addition, a clear and simple method supports a higher level of understanding by more people, as well as increased participation in decisions and greater transparency and operational feasibility. CREFVI should be updated as the context evolves (changes in daily traffic, for example) or when data is defined more precisely (e.g., new flooding analyses). In addition, the vulnerability study is a first step and can be followed by a cost estimate based on possible solutions and

5.2. Limitation of the method and possible improvements The first limitations of our method relate to flooding. The flood mapping method is static (bathtub method), which has been used in a number of recent vulnerability studies due to its simple implementation (Breilh et al., 2013; Johnston et al., 2014; Creach et al., 2015; Muis et al., 2017). By using the levels reached in the field following a major flooding event, we can integrate a component of wave runup in the definition of flooding level, even if it represents averages over homogenous areas. However, new studies will be needed to update this data to obtain a dynamic modeling of flood and adequately include wave runup. As for predicting erosion, erosion hotspots could be incorporated into future projections although such information was not available for our study area. Another limitation of CREFVI is related to hazards affecting roads. 9

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projected intervention periods, as suggested by the U.S. Federal Highway Administration (2008).

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