Coastal Geodynamics

**111**

**1. Introduction**

**Chapter 7**

**Abstract**

Coast of Chile

*María-Victoria Soto, Misael Cabello* 

and is a place of interest for the mining industry.

South-America coastal configuration.

*and Joselyn Arriagada-González*

Current Geodynamics and

Evolutionary Trends of a Headland

Bay Beach System in the Semi-Arid

The Chilean coast is controlled by the tectonics and structure, generating an irregular coastal landscape, with bays, marine terraces, sandy and gravel beaches, sand dune fields and Andean slopes, forming some mega cliffs that are attacked by waves. The Chilean coastline is shaped by headland bay beaches, with a dynamic coast modeled by south-western winds and south–north longshore current. We analyzed the case of the Coquimbo mega headland bay beach, which consists of four headland bay beaches. A methodological study was carried out on the morphometric parameters of the shoreline and the types of beaches dominated by waves along with geomorphological analysis of the coastal zone. We observed a mass transfer process from south to north. The northern sections of the bays are the places with the densest sand dune fields. This concentration of dunes occurs in each bay individually and in the mega bay as well. The sedimentary supply comes from Andean catchments to the shoreline and is transported and reworked by the longshore current to the northern area, where a huge sand field dune has developed, 120 km away from the mouth of Limarí River, the most southern catchment in the study area. In the mega bay, the current trend is a continuous sedimentary supply, despite the semi-arid conditions and the extreme drought that has affected the area since 2011. The study area is also a popular destination in Chile for beach tourism

**Keywords:** headland bay beaches, sand dunes, Andean catchments, sandy supplies

The first approach to headland bay beaches, logarithmic bays and crenulated bays was realized by Halligan (1906 in Ref. [1]), [2–7], and recently by [8–11]. The concept of bays as units for analysis in coastline territories and the theories related to headland bay beaches are particularly important for the comprehension of the geodynamic processes of the Chilean coastline [12], due to the geographical position of the country in the subduction zones of the South-American and Nazca plates, involving a dynamic tectonic movement along the Chilean and western

#### **Chapter 7**

## Current Geodynamics and Evolutionary Trends of a Headland Bay Beach System in the Semi-Arid Coast of Chile

*María-Victoria Soto, Misael Cabello and Joselyn Arriagada-González*

### **Abstract**

The Chilean coast is controlled by the tectonics and structure, generating an irregular coastal landscape, with bays, marine terraces, sandy and gravel beaches, sand dune fields and Andean slopes, forming some mega cliffs that are attacked by waves. The Chilean coastline is shaped by headland bay beaches, with a dynamic coast modeled by south-western winds and south–north longshore current. We analyzed the case of the Coquimbo mega headland bay beach, which consists of four headland bay beaches. A methodological study was carried out on the morphometric parameters of the shoreline and the types of beaches dominated by waves along with geomorphological analysis of the coastal zone. We observed a mass transfer process from south to north. The northern sections of the bays are the places with the densest sand dune fields. This concentration of dunes occurs in each bay individually and in the mega bay as well. The sedimentary supply comes from Andean catchments to the shoreline and is transported and reworked by the longshore current to the northern area, where a huge sand field dune has developed, 120 km away from the mouth of Limarí River, the most southern catchment in the study area. In the mega bay, the current trend is a continuous sedimentary supply, despite the semi-arid conditions and the extreme drought that has affected the area since 2011. The study area is also a popular destination in Chile for beach tourism and is a place of interest for the mining industry.

**Keywords:** headland bay beaches, sand dunes, Andean catchments, sandy supplies

#### **1. Introduction**

The first approach to headland bay beaches, logarithmic bays and crenulated bays was realized by Halligan (1906 in Ref. [1]), [2–7], and recently by [8–11]. The concept of bays as units for analysis in coastline territories and the theories related to headland bay beaches are particularly important for the comprehension of the geodynamic processes of the Chilean coastline [12], due to the geographical position of the country in the subduction zones of the South-American and Nazca plates, involving a dynamic tectonic movement along the Chilean and western South-America coastal configuration.

In this context, and from a morpho-structural perspective, we were able to explain the configuration of irregular coastline associated to tectonics and structural controls in the study area [13–15]. We established the influence of relevant factors in the geometry of the Chilean coastline, what the morphometric conditions of the coastline are, the longshore current, the angle of waves incidence and the types of beaches dominated by waves. These variables were then used to create a morphologic model and a process-response system [16–20]. The impact on the headland bay beaches shows a systematic distribution of wave energy in the longshore current direction. The headland bay beaches are a complex system of mass transfer and form evolutions which are controlled by the structure, the tectonics and the lithology of the area [21, 22].

The study area is Coquimbo Bay, which is located between the mega headland Punta Lengua de Vaca and the sand dunes of Los Choros to the extreme north of the bay. This zone is a headland bay beach system (**Figure 1**). These systems have not been studied much in Chile, the few studies that have been carried out include [1, 17, 18, 21–25].

**Figure 1** shows the general geographical context of the study area. The bays feature the river mouths of the Andean catchment which are the areas that supply mass to the littoral. In the high Andean catchment, some remnant glaciers still remain, with a glacial-snowy-pluvial system, which generates a permanent flow to the Limari and Elqui rivers, despite the intense drought that has been affecting the area since 2010–2011. The catchments (in green) are mainly coastal and subject to rainfall patterns, as well as the low Andean Coastal Range.

In the study area, the big headland of Punta Lengua de Vaca in the north, which is 7.5 km long, plays a role in protecting the bay from SW winds, which show a morpho-sedimentological expression as far away as the big sand dune field, 120 km further north. Between both limits of the Coquimbo mega bay, marine terraces, paleo dunes, beach ridges and sandy beaches are found.

It has been demonstrated that in Chile, sand dunes are concentrated in the northern area of bays, due to the effect of the headlands in wave refraction associated with the prevailing SW–NE winds and the longshore current from the river mouths to the bays [12, 17–19, 21, 23, 25–27].

#### **Figure 1.**

*Geographical context of bays and catchment in the Coquimbo mega bay, Chile. Details of the research environment: Headland bay beach system from Punta Lengua de Vaca headland to Los Choros sandy beach with huge sand dune fields.*

**113**

**3. Littoral zone**

*Geomorphological map of the study area.*

**Figure 2.**

morphometry of the shoreline by [1, 22].

*Current Geodynamics and Evolutionary Trends of a Headland Bay Beach System...*

The geomorphological map of the area of study (**Figure 2**) shows the different groups of identified forms, associated with its location on the western side of the coastal range. The geomorphological map has been created by utilizing existing

The hillslopes of the Coastal Range have been formed by intrusive and volcanic rocks and are in direct contact with the coast. These bays have marine terraces, Pleistocene and Holocene sand dunes, beaches and platforms of active abrasion, reefs, cliffs and small bays of rocks or gravel. These characteristics are very important in the northern area of the mega bay where Cabello [35] identified up to three levels of marine abrasion platforms. These sectors are mostly uninhabited but they are subject to a great deal of economic and environmental interest such as mining extraction activities, protected natural areas such as marine parks and artisanal fishing creeks used by indigenous people (Changos people). These different uses of the land and the sea are not compatible with each other; hence, the area is subject to

The analysis of the littoral zone was achieved by using the classification of wave-dominated beaches by Wright & Short in Ref. [36], complemented with the

bay (N°1 in **Figure 2**) in the southern part of the Coquimbo mega bay has been formed by the effect of the great headland named Punta Lengua de Vaca, which is 7.5 km long. Tongoy is a Reflective beach with low energy, with breaking waves

The mid and southern zones of the area feature bays and sandy beaches. Tongoy

**2. General geomorphology of Coquimbo mega bay**

*DOI: http://dx.doi.org/10.5772/intechopen.94967*

information [28–34] and fieldwork data.

latent environmental conflicts.

*Current Geodynamics and Evolutionary Trends of a Headland Bay Beach System... DOI: http://dx.doi.org/10.5772/intechopen.94967*

#### **2. General geomorphology of Coquimbo mega bay**

*Coastal Environments*

[1, 17, 18, 21–25].

and the lithology of the area [21, 22].

In this context, and from a morpho-structural perspective, we were able to explain the configuration of irregular coastline associated to tectonics and structural controls in the study area [13–15]. We established the influence of relevant factors in the geometry of the Chilean coastline, what the morphometric conditions of the coastline are, the longshore current, the angle of waves incidence and the types of beaches dominated by waves. These variables were then used to create a morphologic model and a process-response system [16–20]. The impact on the headland bay beaches shows a systematic distribution of wave energy in the longshore current direction. The headland bay beaches are a complex system of mass transfer and form evolutions which are controlled by the structure, the tectonics

The study area is Coquimbo Bay, which is located between the mega headland Punta Lengua de Vaca and the sand dunes of Los Choros to the extreme north of the bay. This zone is a headland bay beach system (**Figure 1**). These systems have not been studied much in Chile, the few studies that have been carried out include

**Figure 1** shows the general geographical context of the study area. The bays feature the river mouths of the Andean catchment which are the areas that supply mass to the littoral. In the high Andean catchment, some remnant glaciers still remain, with a glacial-snowy-pluvial system, which generates a permanent flow to the Limari and Elqui rivers, despite the intense drought that has been affecting the area since 2010–2011. The catchments (in green) are mainly coastal and subject to

In the study area, the big headland of Punta Lengua de Vaca in the north, which

is 7.5 km long, plays a role in protecting the bay from SW winds, which show a morpho-sedimentological expression as far away as the big sand dune field, 120 km further north. Between both limits of the Coquimbo mega bay, marine terraces,

It has been demonstrated that in Chile, sand dunes are concentrated in the northern area of bays, due to the effect of the headlands in wave refraction associated with the prevailing SW–NE winds and the longshore current from the river

*Geographical context of bays and catchment in the Coquimbo mega bay, Chile. Details of the research environment: Headland bay beach system from Punta Lengua de Vaca headland to Los Choros sandy beach* 

rainfall patterns, as well as the low Andean Coastal Range.

paleo dunes, beach ridges and sandy beaches are found.

mouths to the bays [12, 17–19, 21, 23, 25–27].

**112**

**Figure 1.**

*with huge sand dune fields.*

The geomorphological map of the area of study (**Figure 2**) shows the different groups of identified forms, associated with its location on the western side of the coastal range. The geomorphological map has been created by utilizing existing information [28–34] and fieldwork data.

The hillslopes of the Coastal Range have been formed by intrusive and volcanic rocks and are in direct contact with the coast. These bays have marine terraces, Pleistocene and Holocene sand dunes, beaches and platforms of active abrasion, reefs, cliffs and small bays of rocks or gravel. These characteristics are very important in the northern area of the mega bay where Cabello [35] identified up to three levels of marine abrasion platforms. These sectors are mostly uninhabited but they are subject to a great deal of economic and environmental interest such as mining extraction activities, protected natural areas such as marine parks and artisanal fishing creeks used by indigenous people (Changos people). These different uses of the land and the sea are not compatible with each other; hence, the area is subject to latent environmental conflicts.

**Figure 2.** *Geomorphological map of the study area.*

#### **3. Littoral zone**

The analysis of the littoral zone was achieved by using the classification of wave-dominated beaches by Wright & Short in Ref. [36], complemented with the morphometry of the shoreline by [1, 22].

The mid and southern zones of the area feature bays and sandy beaches. Tongoy bay (N°1 in **Figure 2**) in the southern part of the Coquimbo mega bay has been formed by the effect of the great headland named Punta Lengua de Vaca, which is 7.5 km long. Tongoy is a Reflective beach with low energy, with breaking waves

smaller than one-meter-height; this Reflective pattern was observed during the 2007–2019 period. The structural influence in the bay can be quantified through the values of the coastal area morphometrics, with a 67° asymmetry angle and a 95° refraction angle (**Figures 3** and **4**).

The neighboring bay, Guanaqueros (N° 3 in **Figure 2**) is a 17 km long sandy beach. The wave dominated-beach type varies between Reflective with low energy, in the south and Intermediate (Longshore Bar through Rhythmic Bar and Beach and Transverse Bar and Rip) in the north. It has an asymmetry angle of 357° in the northern part and 44° in the southern part. Due to the local headland presence, the refraction angle reaches 20° (**Figures 3** and **4**).

Coquimbo Bay has a wide coastline strip of approximately 15 kilometers of sandy beach. This bay is protected by a rocky point which forms Coquimbo's peninsula in the southern part and is an obstacle to the prevailing SW winds and their associated wave action. We observed a systematic distribution of the wave energy from south to north, similar to the theoretical model, which implies a Reflective-Intermediate-Dissipative beach in the southern, center and northern sectors of the bay. From the point of view of the relative position of the shoreline, Coquimbo Bay has an asymmetry angle of 353° and a refraction angle of the surge action of 26° (**Figures 3** and **4**).

The mid-northern area is a rocky coast hosting cliffs and mixed sand and gravel beaches. It has little bays sculpted into the Coastal Range. Morphometric values are variable with asymmetry angles of 6°, 58° and 330°, which illustrate the strong irregularity of this part of the coast.

The wave dominated beaches showed widely varied patterns and were identified as low energy Reflective, Intermediate Transverse Bar and Rip, and Low Tide Terrace (**Figures 3** and **4**). The systematic distribution pattern of wave dominated beaches, Reflective-Intermediate-Dissipative, is hard to verify except for in a few little bays with sandy or gravel beaches. This pattern was not identifiable for rocky beaches with abrasion platforms or reefs because of the alteration of the surge generated by these forms.

**115**

*Current Geodynamics and Evolutionary Trends of a Headland Bay Beach System...*

In the northern area, Los Choros sand dune field is clearly distinguished. The coastline is a 16 km long sandy beach with a big dune field which is 15 km wide, which climbs the hillslope of the Coastal Range. Theses dunes have been stable since

*Coastline morphometry of the mega bay of Coquimbo. Source: Based on [37].*

The northern zone of the mega bay is the accumulation zone of the general system. It is a Dissipative beach, with breaking waves higher than 2 meters. Nonetheless, by analyzing the beach as an individual subsystem we observed some variations in the type of wave-dominated beaches, between Intermediate-Dissipative (2002, 2007, 2013 and 2014). During the years of analysis, we never observed a Reflective state. Villagrán [26] established that this zone shows a bathymetry associated to a lower relative depth, functioning as a trap for sediment entry. The relative position of the coastline (NW-SE orientation), which is transverse to the predominant wind system, shows an asymmetry angle of 311° and a

**Table 1** shows the relationships between the offset angle, the refraction angle of the waves and the type of wave-dominated beaches for 8 bays that have been analyzed. Also, we can see how these latter form the mega bay system, with a dynamic relationship in terms of longshore current direction and associated sediment transfer. Tongoy Bay has an offset angle of 27° in the southern area and 4° in

The dissipation of energy up to the middle and northern zones of each bay is characteristic of the presence of headlands in big bays. The southern zone, with a 95° refraction angle, shows a predominance of wave-dominated beaches of Reflective type and low energy; the middle zone with a rocky coast tends to be more Intermediate and the northern zone, with a 6° refraction angle is Dissipative with high energy, which matches the biggest deposit of dunes in the mega bay. The headland bay beach model is totally applicable to the Coquimbo mega bay. It also

applies to the smaller bays that compose the mega bay's system.

*DOI: http://dx.doi.org/10.5772/intechopen.94967*

Holocene – Pleistocene [38].

**Figure 4.**

refraction angle of 6° (**Figures 3** and **4**).

the northern area (**Figures 3** and **4**).

**Figure 3.** *Wave-dominated beach. Source: Based on [37].*

*Current Geodynamics and Evolutionary Trends of a Headland Bay Beach System... DOI: http://dx.doi.org/10.5772/intechopen.94967*

**Figure 4.** *Coastline morphometry of the mega bay of Coquimbo. Source: Based on [37].*

In the northern area, Los Choros sand dune field is clearly distinguished. The coastline is a 16 km long sandy beach with a big dune field which is 15 km wide, which climbs the hillslope of the Coastal Range. Theses dunes have been stable since Holocene – Pleistocene [38].

The northern zone of the mega bay is the accumulation zone of the general system. It is a Dissipative beach, with breaking waves higher than 2 meters. Nonetheless, by analyzing the beach as an individual subsystem we observed some variations in the type of wave-dominated beaches, between Intermediate-Dissipative (2002, 2007, 2013 and 2014). During the years of analysis, we never observed a Reflective state. Villagrán [26] established that this zone shows a bathymetry associated to a lower relative depth, functioning as a trap for sediment entry. The relative position of the coastline (NW-SE orientation), which is transverse to the predominant wind system, shows an asymmetry angle of 311° and a refraction angle of 6° (**Figures 3** and **4**).

**Table 1** shows the relationships between the offset angle, the refraction angle of the waves and the type of wave-dominated beaches for 8 bays that have been analyzed. Also, we can see how these latter form the mega bay system, with a dynamic relationship in terms of longshore current direction and associated sediment transfer. Tongoy Bay has an offset angle of 27° in the southern area and 4° in the northern area (**Figures 3** and **4**).

The dissipation of energy up to the middle and northern zones of each bay is characteristic of the presence of headlands in big bays. The southern zone, with a 95° refraction angle, shows a predominance of wave-dominated beaches of Reflective type and low energy; the middle zone with a rocky coast tends to be more Intermediate and the northern zone, with a 6° refraction angle is Dissipative with high energy, which matches the biggest deposit of dunes in the mega bay. The headland bay beach model is totally applicable to the Coquimbo mega bay. It also applies to the smaller bays that compose the mega bay's system.

*Coastal Environments*

refraction angle (**Figures 3** and **4**).

irregularity of this part of the coast.

generated by these forms.

refraction angle reaches 20° (**Figures 3** and **4**).

smaller than one-meter-height; this Reflective pattern was observed during the 2007–2019 period. The structural influence in the bay can be quantified through the values of the coastal area morphometrics, with a 67° asymmetry angle and a 95°

The neighboring bay, Guanaqueros (N° 3 in **Figure 2**) is a 17 km long sandy beach. The wave dominated-beach type varies between Reflective with low energy, in the south and Intermediate (Longshore Bar through Rhythmic Bar and Beach and Transverse Bar and Rip) in the north. It has an asymmetry angle of 357° in the northern part and 44° in the southern part. Due to the local headland presence, the

Coquimbo Bay has a wide coastline strip of approximately 15 kilometers of sandy beach. This bay is protected by a rocky point which forms Coquimbo's peninsula in the southern part and is an obstacle to the prevailing SW winds and their associated wave action. We observed a systematic distribution of the wave energy from south to north, similar to the theoretical model, which implies a Reflective-Intermediate-Dissipative beach in the southern, center and northern sectors of the bay. From the point of view of the relative position of the shoreline, Coquimbo Bay has an asymmetry angle of 353° and a refraction angle of the surge action of 26° (**Figures 3** and **4**). The mid-northern area is a rocky coast hosting cliffs and mixed sand and gravel beaches. It has little bays sculpted into the Coastal Range. Morphometric values are variable with asymmetry angles of 6°, 58° and 330°, which illustrate the strong

The wave dominated beaches showed widely varied patterns and were identified as low energy Reflective, Intermediate Transverse Bar and Rip, and Low Tide Terrace (**Figures 3** and **4**). The systematic distribution pattern of wave dominated beaches, Reflective-Intermediate-Dissipative, is hard to verify except for in a few little bays with sandy or gravel beaches. This pattern was not identifiable for rocky beaches with abrasion platforms or reefs because of the alteration of the surge

**114**

**Figure 3.**

*Wave-dominated beach. Source: Based on [37].*


**Table 1.**

*Morphometric parameters in Coquimbo's structural bays system and classification of wave-dominated beaches.*

#### **4. Beach-dune relationship**

Previous studies on headland bay beaches in central Chile have allowed for the establishment of the conditions for a beach-wave interaction system [12, 16–22, 24, 25, 27, 39–41], the morphologic expression of which are foredunes and transgressive dunes. This dynamic system is associated with the coastline orientation and the balancing of internal mass, the changing structure and the balancing of external mass, as well as the relationship with wave-dominated beaches (**Figures 3** and **4**). The presence of foredunes and transgressive dunes are directly related to the availability of sediments from their supplying sources (mainly from the Andean catchment) and also the capacity of transportation in the littoral zone.

By analyzing the geomorphological map (**Figure 2**), we identified that there are foredunes in each bay with sandy beaches. We found the following sequence of forms in Tongoy (**Figure 5**): Holocene vegetated foredunes-terraces with beach ridges-vegetated transverse dunes [39]. In Coquimbo (N°3 in **Figure 2**), there is a sequence of vegetated foredunes and beach ridges (destroyed by urban expansion) and Pleistocene sand dunes. In the extreme north of the mega bay, we observed the following sequence (**Figure 6**): foredunes-active transgressive dunes and the mega field of stabilized sand dunes.

**117**

*Current Geodynamics and Evolutionary Trends of a Headland Bay Beach System...*

*Vegetated foredunes in Los Choros dissipative beach. May 2014. Source: Fondecyt project 1120234.*

In the case of the study area, the permanence of foredunes was noted. Pulse erosion and seasonal deposits existed and we identified that the foredune reconstructs itself [12, 21], thus reinforcing the conditions of a sediment transfer to sandy beaches. This is important to highlight due to the fact that in the area's semi-arid climate there has been a drought for the past decade that has reduced the volume of the Andean catchment. Nonetheless, we have seen evidence of changes in Los Choros dune system, indicating that the development of embryonic dunes and foredunes, which in conjunction with barchan and elongated dunes are evidence of the current supply of sand to the beach.

*Mixed sand and gravel beach, vegetated-foredunes eroded by the waves. Source: Fondecyt project 1120234.*

Another relevant factor is the strong condition of erosion in the mixed sand.

**5. Final considerations in respect to the present dynamic and** 

We analyzed the current geodynamics conditions of the Coquimbo mega bay coastal zone in terms of littoral morphology, beaches and dunes, as indicators of

**evolutionary trend of the Coquimbo mega bay**

south via the longshore current (**Figure 7**).

storms or tsunamis [12].

The condition of a predominantly Dissipative wave-dominated beach is a consequence of the condition of obliquity (4° of offset angle, **Table 1**), a bathymetry of superficial platform that facilitates/provides the transfer of sediments from the

The beach-dune relationship in the system of bays that constitute the Coquimbo mega bay shows a dynamic of constant sand supply leading to a positive sediment budget. This is demonstrated each time that sandy beaches show sequences of beach-active foredunes, even if they are seasonally eroded or affected by offshore

*DOI: http://dx.doi.org/10.5772/intechopen.94967*

**Figure 6.**

**Figure 7.**

*Current Geodynamics and Evolutionary Trends of a Headland Bay Beach System... DOI: http://dx.doi.org/10.5772/intechopen.94967*

#### **Figure 6.**

*Coastal Environments*

7 Caleta

8 Caleta Cruz Grande (Chungungo)

**Table 1.**

Totoralillo Norte

**4. Beach-dune relationship**

field of stabilized sand dunes.

Previous studies on headland bay beaches in central Chile have allowed for the establishment of the conditions for a beach-wave interaction system [12, 16–22, 24, 25, 27, 39–41], the morphologic expression of which are foredunes and transgressive dunes. This dynamic system is associated with the coastline orientation and the balancing of internal mass, the changing structure and the balancing of external mass, as well as the relationship with wave-dominated beaches (**Figures 3** and **4**). The presence of foredunes and transgressive dunes are directly related to the availability of sediments from their supplying sources (mainly from the Andean catchment) and also the capacity of transportation in the littoral zone. By analyzing the geomorphological map (**Figure 2**), we identified that there are foredunes in each bay with sandy beaches. We found the following sequence of forms in Tongoy (**Figure 5**): Holocene vegetated foredunes-terraces with beach ridges-vegetated transverse dunes [39]. In Coquimbo (N°3 in **Figure 2**), there is a sequence of vegetated foredunes and beach ridges (destroyed by urban expansion) and Pleistocene sand dunes. In the extreme north of the mega bay, we observed the following sequence (**Figure 6**): foredunes-active transgressive dunes and the mega

**N° Bay Asymmetry Angle Refraction Angle Off-set**

 Tongoy 67 95 27 R Barnes 31 — 32 R Guanaqueros 44S / 357 N 20 3 R/LTT - TBR/RBB Herradura 40 — 34 R Coquimbo 353 26 16 R/LTT - TBR/

6 Caleta Hornos 330 — 19 TBR

9 Los Choros 311 6 4 D *Reflective (R); Low tide terrace (LTT); Transverse bar & rip (TBR); Rhythmic bar & beach (RBB); Dissipative (D)*

*Morphometric parameters in Coquimbo's structural bays system and classification of wave-dominated beaches.*

**Angle**

58 — 10 R

6 — 14 R

**Waves-dominant Beach**

LTT - D/TBR

*Beach ridge succession in the marine terrace of the Holocene. Source: Fondecyt project 1120234.*

**116**

**Figure 5.**

*Vegetated foredunes in Los Choros dissipative beach. May 2014. Source: Fondecyt project 1120234.*

**Figure 7.** *Mixed sand and gravel beach, vegetated-foredunes eroded by the waves. Source: Fondecyt project 1120234.*

In the case of the study area, the permanence of foredunes was noted. Pulse erosion and seasonal deposits existed and we identified that the foredune reconstructs itself [12, 21], thus reinforcing the conditions of a sediment transfer to sandy beaches. This is important to highlight due to the fact that in the area's semi-arid climate there has been a drought for the past decade that has reduced the volume of the Andean catchment. Nonetheless, we have seen evidence of changes in Los Choros dune system, indicating that the development of embryonic dunes and foredunes, which in conjunction with barchan and elongated dunes are evidence of the current supply of sand to the beach. Another relevant factor is the strong condition of erosion in the mixed sand.

The condition of a predominantly Dissipative wave-dominated beach is a consequence of the condition of obliquity (4° of offset angle, **Table 1**), a bathymetry of superficial platform that facilitates/provides the transfer of sediments from the south via the longshore current (**Figure 7**).

The beach-dune relationship in the system of bays that constitute the Coquimbo mega bay shows a dynamic of constant sand supply leading to a positive sediment budget. This is demonstrated each time that sandy beaches show sequences of beach-active foredunes, even if they are seasonally eroded or affected by offshore storms or tsunamis [12].

#### **5. Final considerations in respect to the present dynamic and evolutionary trend of the Coquimbo mega bay**

We analyzed the current geodynamics conditions of the Coquimbo mega bay coastal zone in terms of littoral morphology, beaches and dunes, as indicators of

**Figure 8.**

*Tongoy beach, oblique section of the Coquimbo mega bay. Erosion of the sandy beach due to the impact of September 2015 tsunami and reconstruction of the same beach in November 2016, showing evidence of sand supply to the beach [12].*

mass transfer. Previous studies have analyzed the relationship between the semi-arid river catchment and the current dynamic coastal processes, focusing mainly on the source areas for sedimentary supply, the pulse of delivery of mass to the shoreline and responses to extreme climatic events. It has been possible to identify an increase in the erosional process in the sandy beaches, nevertheless, with seasonal patterns [27, 42].

The characteristics of wind deposits in the Coquimbo mega bay show that the general distribution of the sand dynamic is replicated in each individual bay and all of them constitute the Coquimbo mega bay where the biggest concentration of sand is accumulated in the northern part of the bay (Los Choros), which corresponds to the oblique zone of the system.

From the dynamic system point of view, Los Choros dune field is very similar to Hesp's scenario model (2013 in Ref. [12]); as a matter of fact, it had a sequence of nearshore-beach-foredunes-transgressive dunes and the evolution of old and present dunes. This dynamic condition has been verified through observation over a 20-year period, showing the permanence of a sandy beach with embryonic dunes and foredunes as evidence of sediment supply.

Foredunes and embryonic dunes are also present in the Reflective low energy beaches of the oblique zone in the south. This condition has been verified as a seasonal dynamic trend. The extreme events of the 2015 tsunami (**Figure 8**), winter and offshore storms over the past.

years have generated a systematic process of destruction of the foredunes which then are newly rebuilt, proving that sediment supply comes from external sources other than the local catchment [12, 21].

The mouth of the Limari River located in the Coastal Range mega cliff does not have a dune deposit matching the size of Andean catchment. As a consequence, it shows that the sand of the Limari river supplies the beaches in the south, Coquimbo partially and mainly the dunes in the north.

#### **Acknowledgements**

Would like to thank Fondecyt for financing the Project "Geodynamics and evolutionary trend of the coastal system of the mega bay of Coquimbo: Towards a prognosis of natural hazards for scenarios of endogenous and exogenous environmental changes" (Fondecyt 1120234).

**119**

Chile

**Author details**

María-Victoria Soto1

\*, Misael Cabello2

\*Address all correspondence to: mvsoto@uchilefau.cl

provided the original work is properly cited.

1 Department of Geography, Universidad de Chile, Santiago, Portugal

2 Lab. of Physical Geography, Department of Geography, Universidad de Chile,

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

and Joselyn Arriagada-González1

*Current Geodynamics and Evolutionary Trends of a Headland Bay Beach System...*

*DOI: http://dx.doi.org/10.5772/intechopen.94967*

*Current Geodynamics and Evolutionary Trends of a Headland Bay Beach System... DOI: http://dx.doi.org/10.5772/intechopen.94967*

### **Author details**

*Coastal Environments*

**Figure 8.**

*supply to the beach [12].*

the oblique zone of the system.

and offshore storms over the past.

**Acknowledgements**

other than the local catchment [12, 21].

mental changes" (Fondecyt 1120234).

partially and mainly the dunes in the north.

and foredunes as evidence of sediment supply.

mass transfer. Previous studies have analyzed the relationship between the semi-arid river catchment and the current dynamic coastal processes, focusing mainly on the source areas for sedimentary supply, the pulse of delivery of mass to the shoreline and responses to extreme climatic events. It has been possible to identify an increase in the erosional process in the sandy beaches, nevertheless, with seasonal patterns [27, 42]. The characteristics of wind deposits in the Coquimbo mega bay show that the general distribution of the sand dynamic is replicated in each individual bay and all of them constitute the Coquimbo mega bay where the biggest concentration of sand is accumulated in the northern part of the bay (Los Choros), which corresponds to

*Tongoy beach, oblique section of the Coquimbo mega bay. Erosion of the sandy beach due to the impact of September 2015 tsunami and reconstruction of the same beach in November 2016, showing evidence of sand* 

From the dynamic system point of view, Los Choros dune field is very similar to Hesp's scenario model (2013 in Ref. [12]); as a matter of fact, it had a sequence of nearshore-beach-foredunes-transgressive dunes and the evolution of old and present dunes. This dynamic condition has been verified through observation over a 20-year period, showing the permanence of a sandy beach with embryonic dunes

Foredunes and embryonic dunes are also present in the Reflective low energy beaches of the oblique zone in the south. This condition has been verified as a seasonal dynamic trend. The extreme events of the 2015 tsunami (**Figure 8**), winter

years have generated a systematic process of destruction of the foredunes which then are newly rebuilt, proving that sediment supply comes from external sources

The mouth of the Limari River located in the Coastal Range mega cliff does not have a dune deposit matching the size of Andean catchment. As a consequence, it shows that the sand of the Limari river supplies the beaches in the south, Coquimbo

Would like to thank Fondecyt for financing the Project "Geodynamics and evolutionary trend of the coastal system of the mega bay of Coquimbo: Towards a prognosis of natural hazards for scenarios of endogenous and exogenous environ-

**118**

María-Victoria Soto1 \*, Misael Cabello2 and Joselyn Arriagada-González1

1 Department of Geography, Universidad de Chile, Santiago, Portugal

2 Lab. of Physical Geography, Department of Geography, Universidad de Chile, Chile

\*Address all correspondence to: mvsoto@uchilefau.cl

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

[1] Araya-Vergara JF. Influencias morfogenéticas de los desalineamientos y líneas de costa contrapuestas en el litoral de Chile Central. Informaciones Geográficas. 1983;23:9-29. DOI: 10.5354/0719-5370.2013.27674.

[2] Jennings JN. The influence of wave action on coastal outline in plan. The Australian Geographer. 1955;6(4):36-44. https://doi.org/10.1080/ 00049185508702306.

[3] Davies JL. Wave refraction and the evolution of shoreline curves. Geogr. Studies. 1959;5:1-14.

[4] Yasso W. Plan geometry of headland bay beaches. Journal of Geology. 1965;73:702-714. DOI: 10.2307/30079652.

[5] Le Blond P. An explanation of the logarithmic spiral plan shape of headland bay beaches. Journal of Sedimentary Petrology. 1979;49(4):1093-1100. https:// doi.org/10.1306/212F78BA-2B24-11D7- 8648000102C1865D.

[6] Hsu JRC, Evans C. Parabolic Bay Shapes and Aplications. Proceedings Institution of Civil Engineers. 1989; 87(2): 556-570. https://doi.org/10.1680/ iicep.1989.3778.

[7] Short A. Handbook of beach and shoreface morphodynamics. Ed. Andrew Short, Wiley. 1999. ISBN: 978-0-471-96570-1.

[8] Rodríguez-Polo S, Del Río L, Benavente J. Longitudinal distribution of slope and sediment characteristics in headland-bay beaches in Cádiz, Spain. Journal of Coastal Research. 2018;85(10085):306-310. DOI: 10.2112/ SI85-062.1.

[9] El-Shinnawy1 A, Medina1 R, González M. Equilibrium planform of headland bay beaches: Effect of directional wave climate. Coastal Dynamics. 2017;20:749-759.

[10] Li B, Zhuang Z, Cao L, Du F. Application of the Static Headland-Bay Beach Concept to a Sandy Beach: A New Elliptical Model. Journal of Ocean University of China. 2020;19:81-89. https://doi.org/10.1007/ s11802-020-3899-1.

[11] Tasaduak S, Weesakul S. Experimental study on dynamic equilibrium of headland-bay beaches. Journal of Coastal Conservation. 2016;20;165-174. https://doi. org/10.1007/s11852-016-0427-y.

[12] Soto MV. Assessment of process dynamics and evolutionary trend of the western part of the arid Chilean coastal range: relationships between river catchments and coastal dynamics of the Coquimbo bay system, Chile [Thesis]. Tubingen: Eberhard Karls Universitat; 2017.

[13] Le Roux JP, Gómez C, Venegas C, Fenner J, Middleton H, Marchant M, Buchbinder B, Frassinetti D, Marquardt C, Gregory-Wodzicki KM, Lavenu A. Neogene–Quaternary coastal and offshore sedimentation in north-central Chile: Record of sea level changes and implications for andean tectonism. Journal of South American Earth Sciences. 2005;19:83-98. https:// doi.org/10.1016/j.jsames.2003.11.003.

[14] Le Roux JP, Olivares D, Nielsen S, Smith N, Middleton H, Fenner J, Ishman S. Bay sedimentation as controlled by regional crustal behavior, local tectonics and eustatic sea level changes: Coquimbo formation (Miocene-Pliocene), bay of Tongoy, central Chile. Journal Sedimentary Geology. 2006;184:133-153. DOI: 10.1016/j.sedgeo.2005.09.023.

[15] Binnie A, Dunai T, Binnie S, Victor P, González G, Bolten A. Accelerated late quaternary uplift revealed by 10Be exposure dating

**121**

*Current Geodynamics and Evolutionary Trends of a Headland Bay Beach System...*

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[24] Soto MV, Arriagada J. Características dinámicas de ensenadas estructurales de Chile centra. Maitencillo-Cachagua y Papudo, Región de Valparaíso. Revista de Geografía Norte Grande. 2007; 38: 99-112. http://dx.doi.org/10.4067/ S0718-34022007000200006.

[25] Martínez C, Quezada M, Rubio P. Historical changes in the shoreline and littoral processes on a headland bay beach in central Chile. Geomorphology.

[26] Villagrán C. Dinámica costera en el sistema de bahías comprendidas entre Ensenada Los Choros y Bahía Tongoy, región de Coquimbo [Thesis]. Santiago:

[27] Soto MV, Märker M, Rodolfi G, Sepúlveda SA, Cabello M. Assessment of geomorphic processes affecting the paleo-landscape of Tongoy bay, Coquimbo region, central Chile. Geografia Fisica e Dinamica Quaternaria. 2014;37(1):51-66. DOI

[28] Paskoff R. Le Chili Semi-aride, recherches geómorphologiques. Bourdeaux: Biscaye Fréres; 1970. 420 p. Traducción al español José Enrique Novoa Jerez. Ediciones Universidad de

[29] Ota Y, Paskoff R. Holoceno deposits on the coast of north – central Chile: radiocarbono ages and implications for coastal changes. Revista Geológica de Chile. 1993;20:25-32. DOI: http://dx.doi.

2011;135:80-96. DOI: 10.1016/j.

geomorph.2011.07.027.

Universidad de Chile; 2007.

10.4461/GFDQ.2014.37.6.

La Serena. La Serena; 1993.

org/10.5027/andgeoV20n1-a03.

[30] Saillard M. Dynamique du soulèvement côtier Pleìstocène des Andes centrales: Etude de l'evolution géomorphologique et datations (10Be)

*DOI: http://dx.doi.org/10.5772/intechopen.94967*

Peninsula, northern Chile. Quaternary Geochronology. 2016;36:12-27. DOI: 10.1016/j.quageo.2016.06.005.

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[17] Martínez C. El efecto de ensenada en los procesos litorales de las ensenadas de Valparaíso, Algarrobo y Cartagena, Chile central [Thesis]. Santiago. Universidad de Chile; 2001.

[18] Soto MV. Aspectos morfodinámicos de ensenadas desalienadas del litoral de Chile central. Pichilemu y Caleta Los Piures. Revista de Geografía Norte Grande. 2005;33:73-87. https:// repositorio.uc.cl/handle/11534/10470.

[19] Arriagada J. Geomorfología estuarial comparada en la zona semiárida de Chile. Casos de Copiapó y Choapa [Thesis]. Santiago. Universidad de

[20] Arriagada J, Soto MV, Sarricolea P. Morphodynamic Environment in a semiarid mouth river complex Choapa river, Chile. In: Marghany M, editor. Handbook of Advanced Geoscience Remote Sensing. Demand: BoD-Books; 2014; p. 254-271. http://dx.doi.

org/10.5772/57410. ch11.

[21] Magallanes V. Relaciones morfodinámicas de la línea de costa entre Tongoy y las dunas de Los Choros. Transferencia sedimentaria en la mega ensenada de Coquimbo [Thesis]. Santiago: Urbanismo, Universidad de

[22] Soto MV, Arriagada J, Cabello, M. The Accretional Beach Ridge System of Tongoy Bay: An Example of a Regressive Barrier Developed in the Semiarid Region of Chile. Recent Advances in Petrochemical Science. *2018;*4(4):001-008. DOI: 10.19080/

RAPSCI.2018.04.555641.

of marine terraces, Mejillones

[16] Araya-Vergara JF. Toward a

DOI: 10.2307/4297157.

Chile; 2009.

Chile; 2017.

*Current Geodynamics and Evolutionary Trends of a Headland Bay Beach System... DOI: http://dx.doi.org/10.5772/intechopen.94967*

of marine terraces, Mejillones Peninsula, northern Chile. Quaternary Geochronology. 2016;36:12-27. DOI: 10.1016/j.quageo.2016.06.005.

[16] Araya-Vergara JF. Toward a classification of beach profiles. Journal of Coastal Research. 1986;2 (2):159-165. DOI: 10.2307/4297157.

[17] Martínez C. El efecto de ensenada en los procesos litorales de las ensenadas de Valparaíso, Algarrobo y Cartagena, Chile central [Thesis]. Santiago. Universidad de Chile; 2001.

[18] Soto MV. Aspectos morfodinámicos de ensenadas desalienadas del litoral de Chile central. Pichilemu y Caleta Los Piures. Revista de Geografía Norte Grande. 2005;33:73-87. https:// repositorio.uc.cl/handle/11534/10470.

[19] Arriagada J. Geomorfología estuarial comparada en la zona semiárida de Chile. Casos de Copiapó y Choapa [Thesis]. Santiago. Universidad de Chile; 2009.

[20] Arriagada J, Soto MV, Sarricolea P. Morphodynamic Environment in a semiarid mouth river complex Choapa river, Chile. In: Marghany M, editor. Handbook of Advanced Geoscience Remote Sensing. Demand: BoD-Books; 2014; p. 254-271. http://dx.doi. org/10.5772/57410. ch11.

[21] Magallanes V. Relaciones morfodinámicas de la línea de costa entre Tongoy y las dunas de Los Choros. Transferencia sedimentaria en la mega ensenada de Coquimbo [Thesis]. Santiago: Urbanismo, Universidad de Chile; 2017.

[22] Soto MV, Arriagada J, Cabello, M. The Accretional Beach Ridge System of Tongoy Bay: An Example of a Regressive Barrier Developed in the Semiarid Region of Chile. Recent Advances in Petrochemical Science. *2018;*4(4):001-008. DOI: 10.19080/ RAPSCI.2018.04.555641.

[23] Araya-Vergara JF. Sistema de interacción oleaje-playa frente a los ergs de Chanco y Arauco, Chile. Gayana Oceanológica. 1996;4(2):159-167.

[24] Soto MV, Arriagada J. Características dinámicas de ensenadas estructurales de Chile centra. Maitencillo-Cachagua y Papudo, Región de Valparaíso. Revista de Geografía Norte Grande. 2007; 38: 99-112. http://dx.doi.org/10.4067/ S0718-34022007000200006.

[25] Martínez C, Quezada M, Rubio P. Historical changes in the shoreline and littoral processes on a headland bay beach in central Chile. Geomorphology. 2011;135:80-96. DOI: 10.1016/j. geomorph.2011.07.027.

[26] Villagrán C. Dinámica costera en el sistema de bahías comprendidas entre Ensenada Los Choros y Bahía Tongoy, región de Coquimbo [Thesis]. Santiago: Universidad de Chile; 2007.

[27] Soto MV, Märker M, Rodolfi G, Sepúlveda SA, Cabello M. Assessment of geomorphic processes affecting the paleo-landscape of Tongoy bay, Coquimbo region, central Chile. Geografia Fisica e Dinamica Quaternaria. 2014;37(1):51-66. DOI 10.4461/GFDQ.2014.37.6.

[28] Paskoff R. Le Chili Semi-aride, recherches geómorphologiques. Bourdeaux: Biscaye Fréres; 1970. 420 p. Traducción al español José Enrique Novoa Jerez. Ediciones Universidad de La Serena. La Serena; 1993.

[29] Ota Y, Paskoff R. Holoceno deposits on the coast of north – central Chile: radiocarbono ages and implications for coastal changes. Revista Geológica de Chile. 1993;20:25-32. DOI: http://dx.doi. org/10.5027/andgeoV20n1-a03.

[30] Saillard M. Dynamique du soulèvement côtier Pleìstocène des Andes centrales: Etude de l'evolution géomorphologique et datations (10Be)

**120**

SI85-062.1.

*Coastal Environments*

**References**

[1] Araya-Vergara JF. Influencias

morfogenéticas de los desalineamientos y líneas de costa contrapuestas en el litoral de Chile Central. Informaciones Geográficas. 1983;23:9-29. DOI: 10.5354/0719-5370.2013.27674.

[10] Li B, Zhuang Z, Cao L, Du F. Application of the Static Headland-Bay Beach Concept to a Sandy Beach: A New Elliptical Model. Journal of Ocean University of China.

s11802-020-3899-1.

2017.

[11] Tasaduak S, Weesakul S. Experimental study on dynamic equilibrium of headland-bay beaches. Journal of Coastal Conservation. 2016;20;165-174. https://doi. org/10.1007/s11852-016-0427-y.

2020;19:81-89. https://doi.org/10.1007/

[12] Soto MV. Assessment of process dynamics and evolutionary trend of the western part of the arid Chilean coastal range: relationships between river catchments and coastal dynamics of the Coquimbo bay system, Chile [Thesis]. Tubingen: Eberhard Karls Universitat;

[13] Le Roux JP, Gómez C, Venegas C, Fenner J, Middleton H, Marchant M,

Marquardt C, Gregory-Wodzicki KM, Lavenu A. Neogene–Quaternary coastal and offshore sedimentation in north-central Chile: Record of sea level changes and implications for andean tectonism. Journal of South American Earth Sciences. 2005;19:83-98. https:// doi.org/10.1016/j.jsames.2003.11.003.

Buchbinder B, Frassinetti D,

[14] Le Roux JP, Olivares D, Nielsen S, Smith N, Middleton H, Fenner J, Ishman S. Bay sedimentation as controlled by regional crustal behavior, local tectonics and eustatic sea level changes: Coquimbo formation (Miocene-Pliocene), bay of Tongoy, central Chile. Journal Sedimentary Geology. 2006;184:133-153. DOI: 10.1016/j.sedgeo.2005.09.023.

[15] Binnie A, Dunai T, Binnie S, Victor P, González G, Bolten A. Accelerated late quaternary uplift revealed by 10Be exposure dating

[2] Jennings JN. The influence of wave action on coastal outline in plan. The Australian Geographer. 1955;6(4):36-44.

[3] Davies JL. Wave refraction and the evolution of shoreline curves. Geogr.

[4] Yasso W. Plan geometry of headland bay beaches. Journal of Geology.

1965;73:702-714. DOI: 10.2307/30079652.

[5] Le Blond P. An explanation of the logarithmic spiral plan shape of headland bay beaches. Journal of Sedimentary Petrology. 1979;49(4):1093-1100. https:// doi.org/10.1306/212F78BA-2B24-11D7-

[6] Hsu JRC, Evans C. Parabolic Bay Shapes and Aplications. Proceedings Institution of Civil Engineers. 1989; 87(2): 556-570. https://doi.org/10.1680/

[7] Short A. Handbook of beach and shoreface morphodynamics. Ed. Andrew Short, Wiley. 1999. ISBN:

[8] Rodríguez-Polo S, Del Río L, Benavente J. Longitudinal distribution of slope and sediment characteristics in headland-bay beaches in Cádiz, Spain. Journal of Coastal Research. 2018;85(10085):306-310. DOI: 10.2112/

[9] El-Shinnawy1 A, Medina1 R, González M. Equilibrium planform of headland bay beaches: Effect of directional wave climate. Coastal Dynamics. 2017;20:749-759.

https://doi.org/10.1080/ 00049185508702306.

Studies. 1959;5:1-14.

8648000102C1865D.

iicep.1989.3778.

978-0-471-96570-1.

de séquences de terrasses marines (sud Pérou – nord Chili [Thesis]). Toulouse: Université de Toulouse; 2008.

[31] Saillard M, Hall SR, Audin L, Farber DL, Hérail G, Martinod J, Regard V, Finkel RC, Bondoux F. Nonsteady long-term uplift rates and Pleistocene marine terrace development along the Andean margin of Chile (31s) inferred from 10Be dating. Earth and Planetary Science Letters. 2009;277:50- 63. https://doi.org/10.1016/j. epsl.2008.09.039.

[32] Saillard M, Hall SR, Audin L, Farber DL, Regard V, Hérail G. Andean coastal uplift and active tectonics in southern Peru: 10Be surface exposure dating of differentially uplifted marine terrace sequences (San Juan de Marcona, W15.4S). Geomorphology. 2011;128:178-190. DOI:10.1016/j. geomorph.2011.01.004.

[33] Saillard M, Riotte J, Regard V, Violette A, Hérail G, Audin L, Riquelme R. Beach ridges ueth dating in Tongoy bay and tectonic implications for a peninsulaebay system, Chile. Journal of South American Earth Sciences. 2012;40:77-84. DOI: 10.1016/j. jsames.2012.09.001.

[34] Pfeiffer J. Evolución y génesis de calcretas pedogénicas en la paleobahía de Tongoy [Thesis]. Santiago: Universidad de Chile; 2011.

[35] Cabello M. Análisis geomorfológico de la sección occidental del Cordón Sarco: Identificación de terrazas marinas. Región de Coquimbo, Chile [Thesis]. Santiago: Universidad de Chile; 2015.

[36] Short AD. Waves-dominated beachs. In: Short, A. (ed). Handbook of beach and shoreface morphodynamics, Chichester: Wiley and Sons; 1999. P. 173-191.

[37] Soto M-V, Arriagada J, Cabello M. Geodinámica y tendencia evolutiva de Chile semiárido costero: la mega ensenada de Coquimbo. In: Borsdorf A, Marchant C, Rovira A, Sánchez R, editors. Handbook Chile cambiando. Revisitando la Geografía Regional de Wolfgang Weischet. Serie GeoLibros N° 36. 2020; p. 343-356. ISBN: 978-956-14-2718-1.

[38] Creixell T, Ortiz L, Arévalo C. Geología Del Área Carrizalillo – El Tofo. Servicio Nacional De Geología y Minería. Mapa geológico, N° 133 y 134, mapa escala 1:100.000, Santiago; 2012.

[39] Lagos G. Caracterización geomorfológica y dinámica costera de bahías del semiárido de Chile. Casos de estudio: Bahía Tongoy y Bahía Barnes, región de Coquimbo [Thesis]. Santiago: Universidad de Chile; 2013.

[40] Benavente N. Relaciones dinámicas asociadas al litoral-playa-duna anteriores del campo de dunas de Los Choros, región de Coquimbo [Thesis]. Santiago: Universidad de Chile; 2015.

[41] Rojas I. Caracterización dinámica de las dunas activas en la Ensenada de Los Choros, IV Región de Coquimbo [Thesis]. Santiago: Universidad de Chile; 2016.

[42] Soto MV, Sarricolea P, Sepúlveda SA, Rodolfi G, Cabello M, Maerker M. Assessment of hydrogeomorphological hazard potentials in the Chilean semiarid coastal range and its impacts on La Serena city, Coquimbo Region. Natural Hazards. 2017;88(1):431-452. DOI: 10.1007/ s11069-017-2873-8.

**123**

Section 6

Coastal Hazards

Section 6 Coastal Hazards

*Coastal Environments*

de séquences de terrasses marines (sud Pérou – nord Chili [Thesis]). Toulouse: [37] Soto M-V, Arriagada J, Cabello M. Geodinámica y tendencia evolutiva de Chile semiárido costero: la mega ensenada de Coquimbo. In: Borsdorf A, Marchant C, Rovira A, Sánchez R, editors. Handbook Chile cambiando. Revisitando la Geografía Regional de Wolfgang Weischet. Serie GeoLibros N° 36. 2020; p. 343-356. ISBN:

[38] Creixell T, Ortiz L, Arévalo C. Geología Del Área Carrizalillo – El Tofo. Servicio Nacional De Geología y Minería. Mapa geológico, N° 133 y 134, mapa escala 1:100.000, Santiago; 2012.

[39] Lagos G. Caracterización

Universidad de Chile; 2013.

[42] Soto MV, Sarricolea P,

s11069-017-2873-8.

Chile; 2016.

asociadas al litoral-playa-duna anteriores del campo de dunas de Los Choros, región de Coquimbo [Thesis]. Santiago: Universidad de Chile; 2015.

geomorfológica y dinámica costera de bahías del semiárido de Chile. Casos de estudio: Bahía Tongoy y Bahía Barnes, región de Coquimbo [Thesis]. Santiago:

[40] Benavente N. Relaciones dinámicas

[41] Rojas I. Caracterización dinámica de las dunas activas en la Ensenada de Los Choros, IV Región de Coquimbo [Thesis]. Santiago: Universidad de

Sepúlveda SA, Rodolfi G, Cabello M, Maerker M. Assessment of hydrogeomorphological hazard potentials in the Chilean semiarid coastal range and its impacts on La Serena city, Coquimbo Region. Natural Hazards. 2017;88(1):431-452. DOI: 10.1007/

978-956-14-2718-1.

Université de Toulouse; 2008.

63. https://doi.org/10.1016/j.

geomorph.2011.01.004.

jsames.2012.09.001.

[32] Saillard M, Hall SR, Audin L, Farber DL, Regard V, Hérail G. Andean coastal uplift and active tectonics in southern Peru: 10Be surface exposure dating of differentially uplifted marine terrace sequences (San Juan de Marcona, W15.4S). Geomorphology. 2011;128:178-190. DOI:10.1016/j.

[33] Saillard M, Riotte J, Regard V, Violette A, Hérail G, Audin L, Riquelme R. Beach ridges ueth dating in Tongoy bay and tectonic implications for a peninsulaebay system, Chile. Journal of South American Earth Sciences. 2012;40:77-84. DOI: 10.1016/j.

[34] Pfeiffer J. Evolución y génesis de calcretas pedogénicas en la paleobahía

[35] Cabello M. Análisis geomorfológico de la sección occidental del Cordón Sarco: Identificación de terrazas marinas. Región de Coquimbo, Chile [Thesis]. Santiago: Universidad de

de Tongoy [Thesis]. Santiago: Universidad de Chile; 2011.

[36] Short AD. Waves-dominated beachs. In: Short, A. (ed). Handbook of beach and shoreface morphodynamics, Chichester: Wiley and Sons; 1999. P.

epsl.2008.09.039.

[31] Saillard M, Hall SR, Audin L, Farber DL, Hérail G, Martinod J, Regard V, Finkel RC, Bondoux F. Nonsteady long-term uplift rates and Pleistocene marine terrace development along the Andean margin of Chile (31s) inferred from 10Be dating. Earth and Planetary Science Letters. 2009;277:50-

**122**

173-191.

Chile; 2015.

**125**

**Chapter 8**

**Abstract**

*Rikito Hisamatsu*

Storm Surge Risk Assessment for

This chapter introduces the efforts of the storm surge risk assessment for non-life insurance especially focusing on Japan. First, the importance of storm surge risk assessment in non-life insurance, the requirements for storm surge risk assessment in insurance, and an overview of the natural disaster model that evaluates them are described. Second, study on stochastic storm surge risk assessment, study on storm surge hazard modeling, study on vulnerability modeling which convert hazard intensity into damage are presented. Third, as an actual calculation example, the results of applying the procedure with low calculation load presented by past study to Tokyo Bay are shown. As a result, it is confirmed that the procedure can reduce the calculation load and maintain the calculation accuracy. Finally, how to select the existing storm surge risk assessment procedures when risk assess-

ment is actually performed for the insurance purposes is considered.

**Keywords:** storm surge, risk assessment procedure, non-life insurance,

flood risk, the risk transfer to non-life insurance has become more critical.

Non-life insurance companies measure the amount of natural disaster risk for their own risk management [4]. Therefore, it is important to properly evaluate the amount of risk. Storm surge damage with strong wind damage accompanying typhoons can be serious when the typhoon is highly intensified, and if damages of wind and storm surge occur at the same time, it may be a peak risk for non-life insurance companies [5]. The Ministry of Land, Infrastructure, Transport and Tourism's Port Bureau published "Guidelines for Storm surge Risk Reduction Policies on Offshore Areas of Ports" in 2018 [6]. Over 80% of the harbor areas in

ment in the insurance industry focusing on Japanese topics.

This section introduces the natural disaster model and background of develop-

In 2016, the Japanese government revised the basic disaster management plan of Japan. According to the updated plan, Japanese government encourages the transfer of flood risk to non-life insurance because future flood risk will increase due to climate change [1]. Globally, economic losses and insurance losses due to natural disasters are on the rise, and the number of wind and flood damage that directly causes insurance losses is increasing significantly [2]. In Japan, on average, over 140 billion yen has been paid annually for insurance due to wind and flood damage since 1991 [3]. With the background of such government trends and increase of wind and

stochastic typhoon model, Tokyo Bay

**1. Introduction**

Non-Life Insurance

#### **Chapter 8**

### Storm Surge Risk Assessment for Non-Life Insurance

*Rikito Hisamatsu*

#### **Abstract**

This chapter introduces the efforts of the storm surge risk assessment for non-life insurance especially focusing on Japan. First, the importance of storm surge risk assessment in non-life insurance, the requirements for storm surge risk assessment in insurance, and an overview of the natural disaster model that evaluates them are described. Second, study on stochastic storm surge risk assessment, study on storm surge hazard modeling, study on vulnerability modeling which convert hazard intensity into damage are presented. Third, as an actual calculation example, the results of applying the procedure with low calculation load presented by past study to Tokyo Bay are shown. As a result, it is confirmed that the procedure can reduce the calculation load and maintain the calculation accuracy. Finally, how to select the existing storm surge risk assessment procedures when risk assessment is actually performed for the insurance purposes is considered.

**Keywords:** storm surge, risk assessment procedure, non-life insurance, stochastic typhoon model, Tokyo Bay

#### **1. Introduction**

This section introduces the natural disaster model and background of development in the insurance industry focusing on Japanese topics.

In 2016, the Japanese government revised the basic disaster management plan of Japan. According to the updated plan, Japanese government encourages the transfer of flood risk to non-life insurance because future flood risk will increase due to climate change [1]. Globally, economic losses and insurance losses due to natural disasters are on the rise, and the number of wind and flood damage that directly causes insurance losses is increasing significantly [2]. In Japan, on average, over 140 billion yen has been paid annually for insurance due to wind and flood damage since 1991 [3]. With the background of such government trends and increase of wind and flood risk, the risk transfer to non-life insurance has become more critical.

Non-life insurance companies measure the amount of natural disaster risk for their own risk management [4]. Therefore, it is important to properly evaluate the amount of risk. Storm surge damage with strong wind damage accompanying typhoons can be serious when the typhoon is highly intensified, and if damages of wind and storm surge occur at the same time, it may be a peak risk for non-life insurance companies [5]. The Ministry of Land, Infrastructure, Transport and Tourism's Port Bureau published "Guidelines for Storm surge Risk Reduction Policies on Offshore Areas of Ports" in 2018 [6]. Over 80% of the harbor areas in

the three major bays of Japan (Tokyo Bay, Ise Bay, and Osaka Bay) are offshore areas of levee, and this guideline has promoted taking measures against storm surges on the areas. Even on the coasts of the three major bays, where the population and assets are particularly concentrated, small storm surges may cause floodwaters and damage many assets.

A major Japanese non-life insurance company has adopted 99.5% Value at Risk as the maximum risk amount that can be directly used for management decisionmaking [4]. In the natural disaster model that measures the risk amount with a low exceedance probability, hazards and economic loss are evaluated based on a probabilistic approach [7].

The history of the introduction of the natural disaster model in the insurance industry is following [8]. Around the 1960s, manual mapping was used for risk management until then, but information technology and Geographic Information System (GIS) have gradually advanced, and the technical base has been established. With regard to data and theory, scientific measurements of natural disasters have made rapid progress since the first half of the 20th century, and studies have been published that theorize the source and frequency of events by the 1970s. A computer-based model (a natural disaster model) for measuring a potential catastrophe was developed by fusing these technological bases, data and theory. Because of Hurricane Andrew in 1992, nine insurance companies could not pay perfectly for insurance claims, and necessity of the natural disaster model had been increased as a basis for decision making in many insurance companies and reinsurance companies. Along with the growing needs of the insurance industry, vendors of natural disaster models has grown and developed.

The natural disaster model used in the insurance industry consists of three modules: hazard module, vulnerability module, and financial module (**Figure 1**) [9]. In the natural disaster model, the hazard module first calculates wind speed, inundation depth, etc., then the vulnerability module converts the information into the damage ratio of the target object such as a house, and finally the financial module calculates the insurance loss considering insurance contract conditions. Natural disaster models continue to be important in insurance industry decision-making [10].

In underwriting, which examines insurance risks, the spatial distribution of inundation depth for each return period may be used [11]. However, the hazard map released by the Japanese government is a result based on the assumed scenario and is not sufficient to utilize it for the purpose of underwriting. Also, the expected annual loss calculated by the natural disaster model is used as a reference when insurance is priced [12].

From the above, the basic requirements to be satisfied by the natural disaster model from the perspective of non-life insurance are as follows.


This Chapter aims to discuss how to select the existing storm surge risk assessment procedures for non-life insurance. In order to discuss this point, review of the procedures including the latest methods with low calculation load and high calculation accuracy are introduced.

**127**

*Storm Surge Risk Assessment for Non-Life Insurance DOI: http://dx.doi.org/10.5772/intechopen.94157*

are described.

**Figure 1.**

**2. Review of storm surge risk assessment methods**

**2.1 Study on stochastic storm surge evaluation**

ensembles of 200 years is used to evaluate the loss.

In this section, previous research and efforts related to storm surge risk assessment

*Components of natural disaster model (based on InterRisk Research Institute & Consulting, Inc. [9] 2013).*

As a storm surge hazard study, Suzuki [13] conducted flood simulation over the whole of Japan, estimated the loss along the coast from the results, and developed loss function which is the relationship between water level at the representative point and the economic loss in the coastal area. The characteristics of this study are that the target area is wide and that large-scale storm surges due to climate change are also incorporated. However, since flood calculation is performed by a simple flood model, there is a issue in the accuracy of flood analysis, and no probabilistic discussion has been made in this study. Examples of publication on probabilistic storm surge damage estimation are following. Tsujita et al. [14] probabilistically calculated storm surge loss in the three major bays in Japan by using stochastic typhoon model. The stochastic typhoon model used in this study statistically processes past typhoon information and calculates the assumed typhoon that will occur in one year for 1000 patterns, so it does not consider future climate. The random variables are typhoon parameters (central pressure, location of typhoon, moving speed). This approach is common to the calculation of insurance purposes, which analyzes natural disaster risk in the coming year in many patterns and uses it for risk management and insurance premium setting. Exceedance probability used for risk management in non-life insurance companies is low. If the calculation period set long, the number of evaluation typhoons will increase and the calculation results will be stable and the uncertainty will decrease. The issue is that there is no discussion about the calculation period that was carried out. In addition, this study did not incorporate levees explicitly in the storm surge simulation, and no discussion on astronomical tide level setting. Similarly, Jiang et al. [15] probabilistically evaluated the current and future climate storm surge losses in the inner part of Ise Bay and showed the relationship between the annual exceedance probability and the storm surge loss. As for the present and future climates, a virtual typhoon for 25

In the previous research mentioned above, it is common that firstly storm surge flood is calculated and secondly the loss is calculated from the inundation depth and the damage function which convert inundation depth into damage rate of the targeted assets. On the other hand, there is not enough discussion about uncertainty. In the process of calculating the storm surge loss, there are many parameters such as astronomical tide level, wind velocity/pressure distribution formula, maximum wind velocity radius, damage function, etc., and it is important to evaluate their effect on the estimated loss. Another issue is that there is no discussion about whether the calculation period is sufficient for the important return period.

*Storm Surge Risk Assessment for Non-Life Insurance DOI: http://dx.doi.org/10.5772/intechopen.94157*

#### **Figure 1.**

*Coastal Environments*

damage many assets.

bilistic approach [7].

models has grown and developed.

decision-making [10].

insurance is priced [12].

underwriting.

tion accuracy are introduced.

the three major bays of Japan (Tokyo Bay, Ise Bay, and Osaka Bay) are offshore areas of levee, and this guideline has promoted taking measures against storm surges on the areas. Even on the coasts of the three major bays, where the population and assets are particularly concentrated, small storm surges may cause floodwaters and

A major Japanese non-life insurance company has adopted 99.5% Value at Risk as the maximum risk amount that can be directly used for management decisionmaking [4]. In the natural disaster model that measures the risk amount with a low exceedance probability, hazards and economic loss are evaluated based on a proba-

The history of the introduction of the natural disaster model in the insurance industry is following [8]. Around the 1960s, manual mapping was used for risk management until then, but information technology and Geographic Information System (GIS) have gradually advanced, and the technical base has been established. With regard to data and theory, scientific measurements of natural disasters have made rapid progress since the first half of the 20th century, and studies have been published that theorize the source and frequency of events by the 1970s. A computer-based model (a natural disaster model) for measuring a potential catastrophe was developed by fusing these technological bases, data and theory. Because of Hurricane Andrew in 1992, nine insurance companies could not pay perfectly for insurance claims, and necessity of the natural disaster model had been increased as a basis for decision making in many insurance companies and reinsurance companies. Along with the growing needs of the insurance industry, vendors of natural disaster

The natural disaster model used in the insurance industry consists of three modules: hazard module, vulnerability module, and financial module (**Figure 1**) [9]. In the natural disaster model, the hazard module first calculates wind speed, inundation depth, etc., then the vulnerability module converts the information into the damage ratio of the target object such as a house, and finally the financial module calculates the insurance loss considering insurance contract conditions. Natural disaster models continue to be important in insurance industry

In underwriting, which examines insurance risks, the spatial distribution of inundation depth for each return period may be used [11]. However, the hazard map released by the Japanese government is a result based on the assumed scenario and is not sufficient to utilize it for the purpose of underwriting. Also, the expected annual loss calculated by the natural disaster model is used as a reference when

From the above, the basic requirements to be satisfied by the natural disaster

3.To be able to create a low-frequency inundation depth map that is used for

This Chapter aims to discuss how to select the existing storm surge risk assessment procedures for non-life insurance. In order to discuss this point, review of the procedures including the latest methods with low calculation load and high calcula-

model from the perspective of non-life insurance are as follows.

1.To be able to predict low-frequency loss.

2.To be able to calculate the expected annual loss.

**126**

*Components of natural disaster model (based on InterRisk Research Institute & Consulting, Inc. [9] 2013).*

#### **2. Review of storm surge risk assessment methods**

In this section, previous research and efforts related to storm surge risk assessment are described.

#### **2.1 Study on stochastic storm surge evaluation**

As a storm surge hazard study, Suzuki [13] conducted flood simulation over the whole of Japan, estimated the loss along the coast from the results, and developed loss function which is the relationship between water level at the representative point and the economic loss in the coastal area. The characteristics of this study are that the target area is wide and that large-scale storm surges due to climate change are also incorporated. However, since flood calculation is performed by a simple flood model, there is a issue in the accuracy of flood analysis, and no probabilistic discussion has been made in this study. Examples of publication on probabilistic storm surge damage estimation are following. Tsujita et al. [14] probabilistically calculated storm surge loss in the three major bays in Japan by using stochastic typhoon model. The stochastic typhoon model used in this study statistically processes past typhoon information and calculates the assumed typhoon that will occur in one year for 1000 patterns, so it does not consider future climate. The random variables are typhoon parameters (central pressure, location of typhoon, moving speed). This approach is common to the calculation of insurance purposes, which analyzes natural disaster risk in the coming year in many patterns and uses it for risk management and insurance premium setting. Exceedance probability used for risk management in non-life insurance companies is low. If the calculation period set long, the number of evaluation typhoons will increase and the calculation results will be stable and the uncertainty will decrease. The issue is that there is no discussion about the calculation period that was carried out. In addition, this study did not incorporate levees explicitly in the storm surge simulation, and no discussion on astronomical tide level setting. Similarly, Jiang et al. [15] probabilistically evaluated the current and future climate storm surge losses in the inner part of Ise Bay and showed the relationship between the annual exceedance probability and the storm surge loss. As for the present and future climates, a virtual typhoon for 25 ensembles of 200 years is used to evaluate the loss.

In the previous research mentioned above, it is common that firstly storm surge flood is calculated and secondly the loss is calculated from the inundation depth and the damage function which convert inundation depth into damage rate of the targeted assets. On the other hand, there is not enough discussion about uncertainty. In the process of calculating the storm surge loss, there are many parameters such as astronomical tide level, wind velocity/pressure distribution formula, maximum wind velocity radius, damage function, etc., and it is important to evaluate their effect on the estimated loss. Another issue is that there is no discussion about whether the calculation period is sufficient for the important return period.

A representative example of storm surge risk assessment efforts is the HUZUS-MH (Hazards U.S. Multi-Hazard) [16] developed by the Federal Emergency Management Agency (FEMA). This is software that estimates the damage caused by earthquakes, hurricanes, and floods in the United States, and displays damage of buildings and infrastructure due to past hazards. In addition, for the flood insurance program in the United States, FEMA has created a storm surge risk map with annual exceedance probability of 10, 2, 1, 0.2% in the United States, and has evaluated storm surge risk by various methods [17]. As a method, extreme value analysis, EST (Empirical Simulation Technique), and JPM (Joint Probability Method) have been studied. EST is a method of estimating the occurrence probability of the water level based on the observed tide level at a certain point by Probability distribution and constructing an artificial event set by the bootstrap method. JPM captures characteristics of hurricanes from observation information, constructs possible hurricanes from probability distributions of central pressure, moving speed and so on, and performs storm surge numerical calculation for all of them. While EST depends on limited observation data, JPM can comprehensively consider hurricanes etc. that may occur, and in recent years, JPM approach has been recognized as suitable for stochastic evaluation [17].

Similarly, in the natural disaster model of the insurance industry and probabilistic storm surge risk assessment in academic research, the method of calculating storm surges using assumed typhoons that capture past typhoon characteristics like JPM for a long term such as 1 year × 10,000 patterns has become common (eg AIRWORLDWIDE [18], Risk Management Solutions [19], Tsujita et al. [14]). That is, for example, when analyzing typhoons stochastically in Japan, it is assumed that a total of about 30,000 typhoons will land for 10,000 years because about 3 typhoons land in one year in average. However, it has been pointed out that the issue is that the computational cost is high because JPM calculates storm surges for all possible typhoons [17].

In order to reduce the calculation load of storm surge simulation, Jiang et al. [15] limited the typhoons that numerically calculate storm surge under the following three conditions.


Through this process, typhoons that can flood are extracted. However, when considering variations in astronomical tide levels, it is not sufficient to evaluate storm surge risk because storm surge risk also depends on astronomical tide level, and it is necessary to use the total water level considering tides. In addition, the discussion on how much small and medium-scale storm surge damage can be extracted and uncertainty of annual expected loss of storm surge are insufficient in this paper.

In the US, FEMA has developed and is currently using JPM-OS (Joint Probability Method – Optimal Sampling), which is a method that reduces the calculation load of JPM (Johnson et al. [20]). Here, JPM-OS is briefly described. First, JPM-OS performs storm surge inundation analysis only on representative events selected from the numerous typhoon events. Then, the results are interpolated to estimate the inundation depth of the event for which no flood analysis has been performed. Although it is possible to reduce the calculation load by one digit compared to JPM [17], the problem is that

**129**

**Figure 2.**

*Storm Surge Risk Assessment for Non-Life Insurance DOI: http://dx.doi.org/10.5772/intechopen.94157*

Bay, Japan are shown in Section 3.

numerical simulation model for storm surge [24].

**2.2 Study on hazard modeling**

are introduced.

uncertainty arises during interpolation. In order to reduce the uncertainty, research is underway on JPM-OS interpolation methods (eg Yang et al. [21]). Yang et al. compared the calculation results of the inundation depth for different JPM-OS interpolation method for each return period in Florida, USA. The RMSE of each method was 0.16 to 0.82 m when the inundation depth at each point was compared with the calculation result by JPM for each interpolation method for the return period of 50, 100, and 500 years. It was shown that an error occurs between the interpolated result and the numerical calculation result regardless of whether the return period is long or short. As another approach, Hisamatsu et al. [5] selected a typhoon that can be inundated by a simple formula for typhoons of the stochastic typhoon model, and calculated the storm surge inundation only for the selected typhoon using a numerical model. Procedure by Hisamatsu et al. is described in **Figure 2**. This method aims at both reduction of calculation load and preservation of calculation accuracy by extracting floodable typhoons. Additionally, fluctuation of astronomical tide can be considered. The brief results of applying this procedure to the Tokyo

There are various storm surge models all over the world, but here some of them

As a method for numerical analysis of storm surge hazards, a lot of studies have shown that considering wave set-up improves reproducibility. Kim et al. [22] developed a SuWAT (Surge-Wave-Tide coupled model), which is a model that considers wave set-up. SuWAT is a two-way coupled model that considers the interaction between tidal, storm surge and wave. The SuWAT model, composed of depthintegrated nonlinear shallow water equations and a simulated-waves near-shore (SWAN) model, can simultaneously run an arbitrary number of nested domains by using the message passing interface (MPI). Mase et al. [23] simulated typhoon Vera 1959 using SuWAT, and showed that the reproducibility of the storm surge is greatly improved if wave set-up is incorporated explicitly. Since SuWAT has been used by a lot of research especially in Japan and other Asian regions (eg Hisamatsu et al. [5]), this model has been adopted to natural disaster model of the insurance industry as

*Procedure of stochastic storm surge proposed by Hisamatsu et al. (based on Hisamatsu et al. [5] 2020).*

*Storm Surge Risk Assessment for Non-Life Insurance DOI: http://dx.doi.org/10.5772/intechopen.94157*

*Coastal Environments*

A representative example of storm surge risk assessment efforts is the HUZUS-MH (Hazards U.S. Multi-Hazard) [16] developed by the Federal

been recognized as suitable for stochastic evaluation [17].

surges for all possible typhoons [17].

• Typhoons that pass within 100 km of the bay

• Typhoons with a minimum central pressure of 950 hPa or less

• Typhoons with a typhoon speed of 20 km/h or more when landing

Through this process, typhoons that can flood are extracted. However, when considering variations in astronomical tide levels, it is not sufficient to evaluate storm surge risk because storm surge risk also depends on astronomical tide level, and it is necessary to use the total water level considering tides. In addition, the discussion on how much small and medium-scale storm surge damage can be extracted and uncertainty of annual expected loss of storm surge are insufficient in

In the US, FEMA has developed and is currently using JPM-OS (Joint Probability Method – Optimal Sampling), which is a method that reduces the calculation load of JPM (Johnson et al. [20]). Here, JPM-OS is briefly described. First, JPM-OS performs storm surge inundation analysis only on representative events selected from the numerous typhoon events. Then, the results are interpolated to estimate the inundation depth of the event for which no flood analysis has been performed. Although it is possible to reduce the calculation load by one digit compared to JPM [17], the problem is that

three conditions.

Emergency Management Agency (FEMA). This is software that estimates the damage caused by earthquakes, hurricanes, and floods in the United States, and displays damage of buildings and infrastructure due to past hazards. In addition, for the flood insurance program in the United States, FEMA has created a storm surge risk map with annual exceedance probability of 10, 2, 1, 0.2% in the United States, and has evaluated storm surge risk by various methods [17]. As a method, extreme value analysis, EST (Empirical Simulation Technique), and JPM (Joint Probability Method) have been studied. EST is a method of estimating the occurrence probability of the water level based on the observed tide level at a certain point by Probability distribution and constructing an artificial event set by the bootstrap method. JPM captures characteristics of hurricanes from observation information, constructs possible hurricanes from probability distributions of central pressure, moving speed and so on, and performs storm surge numerical calculation for all of them. While EST depends on limited observation data, JPM can comprehensively consider hurricanes etc. that may occur, and in recent years, JPM approach has

Similarly, in the natural disaster model of the insurance industry and probabilistic storm surge risk assessment in academic research, the method of calculating storm surges using assumed typhoons that capture past typhoon characteristics like JPM for a long term such as 1 year × 10,000 patterns has become common (eg AIRWORLDWIDE [18], Risk Management Solutions [19], Tsujita et al. [14]). That is, for example, when analyzing typhoons stochastically in Japan, it is assumed that a total of about 30,000 typhoons will land for 10,000 years because about 3 typhoons land in one year in average. However, it has been pointed out that the issue is that the computational cost is high because JPM calculates storm

In order to reduce the calculation load of storm surge simulation, Jiang et al. [15] limited the typhoons that numerically calculate storm surge under the following

**128**

this paper.

uncertainty arises during interpolation. In order to reduce the uncertainty, research is underway on JPM-OS interpolation methods (eg Yang et al. [21]). Yang et al. compared the calculation results of the inundation depth for different JPM-OS interpolation method for each return period in Florida, USA. The RMSE of each method was 0.16 to 0.82 m when the inundation depth at each point was compared with the calculation result by JPM for each interpolation method for the return period of 50, 100, and 500 years. It was shown that an error occurs between the interpolated result and the numerical calculation result regardless of whether the return period is long or short.

As another approach, Hisamatsu et al. [5] selected a typhoon that can be inundated by a simple formula for typhoons of the stochastic typhoon model, and calculated the storm surge inundation only for the selected typhoon using a numerical model. Procedure by Hisamatsu et al. is described in **Figure 2**. This method aims at both reduction of calculation load and preservation of calculation accuracy by extracting floodable typhoons. Additionally, fluctuation of astronomical tide can be considered. The brief results of applying this procedure to the Tokyo Bay, Japan are shown in Section 3.

#### **2.2 Study on hazard modeling**

There are various storm surge models all over the world, but here some of them are introduced.

As a method for numerical analysis of storm surge hazards, a lot of studies have shown that considering wave set-up improves reproducibility. Kim et al. [22] developed a SuWAT (Surge-Wave-Tide coupled model), which is a model that considers wave set-up. SuWAT is a two-way coupled model that considers the interaction between tidal, storm surge and wave. The SuWAT model, composed of depthintegrated nonlinear shallow water equations and a simulated-waves near-shore (SWAN) model, can simultaneously run an arbitrary number of nested domains by using the message passing interface (MPI). Mase et al. [23] simulated typhoon Vera 1959 using SuWAT, and showed that the reproducibility of the storm surge is greatly improved if wave set-up is incorporated explicitly. Since SuWAT has been used by a lot of research especially in Japan and other Asian regions (eg Hisamatsu et al. [5]), this model has been adopted to natural disaster model of the insurance industry as numerical simulation model for storm surge [24].

#### **Figure 2.**

*Procedure of stochastic storm surge proposed by Hisamatsu et al. (based on Hisamatsu et al. [5] 2020).*

In flood calculation, models that improve the calculation speed have been used. For example, Ramirez et al. [25] used LISFLOOD-FP [26] for flood calculation, which is dynamic and has a small calculation load. Inundation calculation load of storm surge was further reduced by using the flood model with the results of the storm surge model as boundary conditions (eg. Tsujita et al. [14]).

#### **2.3 Study on vulnerability modeling**

Some existing research on the damage function, which estimates the damage of assets from the inundation depth, are presented.

Regarding the relationship between tsunami inundation depth and its damage, as a representative of the tsunami damage caused by the 2011 Great East Japan Earthquake, the fragility curves expressing the probability of occurrence for each degree of damage have been published (eg Suppasri et al. [27], Aránguiz et al. [28]). These fragility curves are functions that calculate the occurrence probability *P*i of each damage level *i* (minor, moderate, major, complete) using the inundation depth *η* as an explanatory variable as shown in the following equation (Eq. (1)).

$$P\_i = f\_i(\eta) \tag{1}$$

However, since this fragility curve does not show the damage ratio of assets, it is not possible to directly calculate the loss from the hazard intensity of the tsunami. Dias et al. [29] presented a method of converting the fragility curves into damage function. In other words, the tsunami fragility curves that have been accumulated so far can be converted into a tsunami damage function that can directly calculate the asset damage ratio *R* using the inundation depth as an explanatory variable, as shown in the following equation (Eq. (2)).

$$R = f(\eta) \tag{2}$$

**131**

**Figure 3.**

*Area where typhoon is extracted (Hisamatsu et al. [37] 2020).*

*Storm Surge Risk Assessment for Non-Life Insurance DOI: http://dx.doi.org/10.5772/intechopen.94157*

**3. Case study in the Tokyo Bay, Japan**

while US Army Corps of Engineers [32], Suzuki et al. [34] and Hisamatsu et al. [35] are the latter. In other cases, such as Tsujita et al. [14], the step function of the MLIT is regressed and converted into a continuous function and is used for damage estimation. In the case of the step function, since the damage function is constructed by setting the damage of modeled building according to the inundation depth, the function that the damage increases when the water level reaches the floor or ceiling of the modeled building. However, the structures of buildings that are actually damaged vary, damage ratio at the same inundation depth differs depending on the building. Therefore, the damage functions of US Army Corps of Engineers [32] and Suzuki et al. [34], which were constructed based on the disaster survey, is a continuous

The storm surge risk assessment procedure described in **Figure 2** was applied to the Tokyo Bay where assets are concentrated in Japan in order to whether the calculation accuracy can be maintained by reducing the calculation load is considered. As a stochastic typhoon model, global stochastic typhoon model (GSTM) developed by Nakajo et al. [36] is used for evaluation. Typhoons created by the GSTM is extracted around Tokyo Bay (**Figure 3**), and top 1000 typhoons are used as input data of numerical model SuWAT following proposed procedure. Prior to apply SuWAT, reproductivity of the model is validated by calculating time series storm surge levels of Typhoon Irma along the Tokyo Bay coast. The astronomical tide level

damage function because it covers buildings with various structures.

The storm surge damage function of HAZUS in the United States is frequently used to estimate the storm surge damage amount, which converts the storm surge inundation depth into asset damage rate (eg, Johnson et al. [20], Lin et al. [30]). The damage function installed in HAZUS was developed by US Army Corps of Engineers through post-flood research and interviews with experts [31, 32]. In addition, Kar and Hodgson [33] theoretically constructed a storm surge damage function.

In Japan, damage functions in Manual of Economic Survey for Water Management published by Ministry of Land, Infrastructure, Transport and Tourism (MLIT), Japan is widely used (eg Hisamatsu et al. [5], Tsujita et al. [14], Jiang et al. [15]). The reason is that there is no other storm surge damage function for Japanese assets. This was constructed based on the survey conducted in 1993 to 1996, and the issue is that the information of the material and equipment of the house, etc. surveyed deviates from present. Therefore, some study described relationships between inundation depth and damage ratio based on survey and simulation as following. Suzuki et al. developed flood damage function by hearing-based survey [34]. And Hisamatsu et al. developed flood damage function using the result of flood simulation and insurance data [35]. Unfortunately, small number of studies on damage functions is conducted. However, it is possible to accumulate storm surge damage functions based on the approach described above for actual events.

The shape of the damage function is roughly divided into two types, a step function and a continuous function. Kar and Hodgson [33] and the MLIT are the former, *Storm Surge Risk Assessment for Non-Life Insurance DOI: http://dx.doi.org/10.5772/intechopen.94157*

*Coastal Environments*

**2.3 Study on vulnerability modeling**

assets from the inundation depth, are presented.

shown in the following equation (Eq. (2)).

In flood calculation, models that improve the calculation speed have been used. For example, Ramirez et al. [25] used LISFLOOD-FP [26] for flood calculation, which is dynamic and has a small calculation load. Inundation calculation load of storm surge was further reduced by using the flood model with the results of the

Some existing research on the damage function, which estimates the damage of

Regarding the relationship between tsunami inundation depth and its damage, as a representative of the tsunami damage caused by the 2011 Great East Japan Earthquake, the fragility curves expressing the probability of occurrence for each degree of damage have been published (eg Suppasri et al. [27], Aránguiz et al. [28]). These fragility curves are functions that calculate the occurrence probability *P*i of each damage level *i* (minor, moderate, major, complete) using the inundation depth

*η* as an explanatory variable as shown in the following equation (Eq. (1)).

*P f i i* = (h

*R f* = (h

The storm surge damage function of HAZUS in the United States is frequently used to estimate the storm surge damage amount, which converts the storm surge inundation depth into asset damage rate (eg, Johnson et al. [20], Lin et al. [30]). The damage function installed in HAZUS was developed by US Army Corps of Engineers through post-flood research and interviews with experts [31, 32]. In addition, Kar and Hodgson [33] theoretically constructed a storm surge damage function. In Japan, damage functions in Manual of Economic Survey for Water Management published by Ministry of Land, Infrastructure, Transport and Tourism (MLIT), Japan is widely used (eg Hisamatsu et al. [5], Tsujita et al. [14], Jiang et al. [15]). The reason is that there is no other storm surge damage function for Japanese assets. This was constructed based on the survey conducted in 1993 to 1996, and the issue is that the information of the material and equipment of the house, etc. surveyed deviates from present. Therefore, some study described relationships between inundation depth and damage ratio based on survey and simulation as following. Suzuki et al. developed flood damage function by hearing-based survey [34]. And Hisamatsu et al. developed flood damage function using the result of flood simulation and insurance data [35]. Unfortunately, small number of studies on damage functions is conducted. However, it is possible to accumulate storm surge damage functions based on the approach described above for actual events. The shape of the damage function is roughly divided into two types, a step function and a continuous function. Kar and Hodgson [33] and the MLIT are the former,

However, since this fragility curve does not show the damage ratio of assets, it is not possible to directly calculate the loss from the hazard intensity of the tsunami. Dias et al. [29] presented a method of converting the fragility curves into damage function. In other words, the tsunami fragility curves that have been accumulated so far can be converted into a tsunami damage function that can directly calculate the asset damage ratio *R* using the inundation depth as an explanatory variable, as

) (1)

) (2)

storm surge model as boundary conditions (eg. Tsujita et al. [14]).

**130**

while US Army Corps of Engineers [32], Suzuki et al. [34] and Hisamatsu et al. [35] are the latter. In other cases, such as Tsujita et al. [14], the step function of the MLIT is regressed and converted into a continuous function and is used for damage estimation. In the case of the step function, since the damage function is constructed by setting the damage of modeled building according to the inundation depth, the function that the damage increases when the water level reaches the floor or ceiling of the modeled building. However, the structures of buildings that are actually damaged vary, damage ratio at the same inundation depth differs depending on the building. Therefore, the damage functions of US Army Corps of Engineers [32] and Suzuki et al. [34], which were constructed based on the disaster survey, is a continuous damage function because it covers buildings with various structures.

#### **3. Case study in the Tokyo Bay, Japan**

The storm surge risk assessment procedure described in **Figure 2** was applied to the Tokyo Bay where assets are concentrated in Japan in order to whether the calculation accuracy can be maintained by reducing the calculation load is considered.

As a stochastic typhoon model, global stochastic typhoon model (GSTM) developed by Nakajo et al. [36] is used for evaluation. Typhoons created by the GSTM is extracted around Tokyo Bay (**Figure 3**), and top 1000 typhoons are used as input data of numerical model SuWAT following proposed procedure. Prior to apply SuWAT, reproductivity of the model is validated by calculating time series storm surge levels of Typhoon Irma along the Tokyo Bay coast. The astronomical tide level

**Figure 3.** *Area where typhoon is extracted (Hisamatsu et al. [37] 2020).*

was calculated by using the harmonic constants to estimate the time series tide level at the Tokyo tidal station for 100 years from January 2000 to December 2099. The astronomical tide level for each typhoon was set by randomly extracting from this histogram of calculated astronomical tide levels. From above calculation, storm surge inundation depth distributions due to 1000 typhoons are obtained. Example of the results is shown in **Figure 4**.

**133**

**Figure 5.**

*Annual exceedance probability curve.*

*Storm Surge Risk Assessment for Non-Life Insurance DOI: http://dx.doi.org/10.5772/intechopen.94157*

ducted by Hisamatsu et al. [37] is introduced.

1000 typhoons extracted in the same way.

In addition, economic loss is estimated by using the inundation depth calculated and damage functions by MLIT. Targeted assets are houses and business establishments and loss calculation consider number of floors. By using calculated loss amount, exceedance probability curve is created as described in **Figure 5**.

From the above calculations, it was confirmed that the insurance requirements shown in Section 1 are satisfied by applying suggested procedure. However, it is necessary to consider whether or not calculation accuracy can be maintained by reducing the calculation load following the procedure. Here, consideration con-

The storm surge loss introduced in this section was estimated by extracting typhoons from the stochastic typhoon model based on the top 1000 water levels by the storm surge empirical formula. The maximum water level by the storm surge empirical formula used in the extraction process is different from the numerical model result. Therefore, the ranking of the maximum water level in the Tokyo Bay differs between the numerical model and the empirical formula. It is important to check whether the number of events extracted by empirical formula was sufficient for insurance purposes, in order to confirm the usefulness of the proposed procedure. **Figure 6** shows the estimated loss by rank for each number of extracted typhoons. The horizontal axis shows the number of typhoons extracted from the top of the total water level based on the empirical formula, in other words, number of typhoons used for the analysis, and confirms how many typhoons the loss amount of the target order will converge. In the analysis based on 1000 typhoons, the losses in the top 50 and above were almost converged. It was suggested that 1000 typhoons based on the proposed procedure are generally sufficient to obtain low-frequency damage amounts for the purpose of insurance. In addition, it was confirmed that the infrequent water levels in Chiba, Yokohama, and Yokosuka would converge with

According to discuss the annual expected loss amount, it was confirmed that the annual expected loss amount not being fully converged. It was found that the reason was the creation of the asset amount distribution. Since the amount of assets is created from statistical information, the resolution is coarser than that of storm surge numerical analysis. Because the statistical information is distributed according to the resolution of the numerical analysis, it is distributed to the place where the asset originally does not exist and the loss amount is calculated.

**Figure 4.** *Example of simulation result.*

*Coastal Environments*

of the results is shown in **Figure 4**.

was calculated by using the harmonic constants to estimate the time series tide level at the Tokyo tidal station for 100 years from January 2000 to December 2099. The astronomical tide level for each typhoon was set by randomly extracting from this histogram of calculated astronomical tide levels. From above calculation, storm surge inundation depth distributions due to 1000 typhoons are obtained. Example

**132**

**Figure 4.**

*Example of simulation result.*

In addition, economic loss is estimated by using the inundation depth calculated and damage functions by MLIT. Targeted assets are houses and business establishments and loss calculation consider number of floors. By using calculated loss amount, exceedance probability curve is created as described in **Figure 5**.

From the above calculations, it was confirmed that the insurance requirements shown in Section 1 are satisfied by applying suggested procedure. However, it is necessary to consider whether or not calculation accuracy can be maintained by reducing the calculation load following the procedure. Here, consideration conducted by Hisamatsu et al. [37] is introduced.

The storm surge loss introduced in this section was estimated by extracting typhoons from the stochastic typhoon model based on the top 1000 water levels by the storm surge empirical formula. The maximum water level by the storm surge empirical formula used in the extraction process is different from the numerical model result. Therefore, the ranking of the maximum water level in the Tokyo Bay differs between the numerical model and the empirical formula. It is important to check whether the number of events extracted by empirical formula was sufficient for insurance purposes, in order to confirm the usefulness of the proposed procedure. **Figure 6** shows the estimated loss by rank for each number of extracted typhoons. The horizontal axis shows the number of typhoons extracted from the top of the total water level based on the empirical formula, in other words, number of typhoons used for the analysis, and confirms how many typhoons the loss amount of the target order will converge. In the analysis based on 1000 typhoons, the losses in the top 50 and above were almost converged. It was suggested that 1000 typhoons based on the proposed procedure are generally sufficient to obtain low-frequency damage amounts for the purpose of insurance. In addition, it was confirmed that the infrequent water levels in Chiba, Yokohama, and Yokosuka would converge with 1000 typhoons extracted in the same way.

According to discuss the annual expected loss amount, it was confirmed that the annual expected loss amount not being fully converged. It was found that the reason was the creation of the asset amount distribution. Since the amount of assets is created from statistical information, the resolution is coarser than that of storm surge numerical analysis. Because the statistical information is distributed according to the resolution of the numerical analysis, it is distributed to the place where the asset originally does not exist and the loss amount is calculated.

**Figure 5.** *Annual exceedance probability curve.*

**Figure 6.** *Estimated loss by rank for each number of extracted typhoons (based on Hisamatsu et al. [37] 2020).*

On the other hand, the places where the number of floods was extremely high in the numerical calculation of storm surges are waterside areas where no assets actually exist. As a result of estimating the loss amount ignoring the loss at these areas, it was confirmed that the expected annual loss amount has converged. Therefore, if the asset amount distribution can be corrected more realistic this problem will be solved. It also suggests that asset allocation is very important for risk assessment.

#### **4. Conclusion**

In this chapter, the efforts of storm surge risk assessment in the non-life insurance industry are introduced based on the author's experience. In the insurance industry, probabilistic storm surge risk assessment is required for risk management and underwriting, and a lot of analysis is required. Therefore, how to reduce the calculation load without degrading the calculation accuracy is being discussed. In this chapter, author introduced a previous study on reducing computational load. In particular, an example in which the procedure of numerically calculating only typhoons that can cause floods was applied to Tokyo Bay is introduced.

This procedure is useful in two ways. The first point is to significantly reduce the calculation load. The procedure aims at both the reduction of the calculation load and the reduction of the calculation error compared to other methods. By applying the procedure, it was found that for Tokyo Bay, the number of typhoons to be calculated can be reduced from about 90,000 to 1000 and the calculation accuracy can be maintained. By utilizing this procedure, the cost of storm surge risk assessment can be reduced, and the reduction of insurance rate may be possible, so that taking out insurance and transferring risks to ensure a safer life for more people are expected. In addition, even if the stochastic typhoon model is updated, the risk can be quickly evaluated based on the latest knowledge considering climate change and reflected in the risk management of the insurance company. The second point is that the loss amount can be evaluated more appropriately by considering the variation of the astronomical tide level. The uncertainty associated with risk assessment for insurance purposes can be recognized and reduced by varying the astronomical tide level.

In the insurance industry, the procedure applied to the Tokyo Bay in this chapter and the method like JPM-OS should be used separately. First, the characteristics of the evaluation target site should be considered. Since the procedure applied to the Tokyo Bay analyzes storm surges for all typhoons that may flood, it has a great effect

**135**

**Author details**

Rikito Hisamatsu

MS&AD InterRisk Research and Consulting, Inc., Tokyo, Japan

\*Address all correspondence to: rikito.hisamatsu@ms-ad-hd.com

provided the original work is properly cited.

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*Storm Surge Risk Assessment for Non-Life Insurance DOI: http://dx.doi.org/10.5772/intechopen.94157*

and evaluate with high accuracy.

Inc. for their comments and supports.

The authors declare no conflict of interest.

**Acknowledgements**

**Conflict of interest**

on reducing computational load in areas where flooding is unlikely to occur, such as the Tokyo Bay. However, when targeting areas with high flooding frequency, such as Southeast Asia, the number of typhoons that can be flooded will be huge, and the effect of reducing computational load cannot be expected. Therefore, it is necessary to judge the risk assessment by JPM-OS allowing the calculation accuracy in such areas with high flood frequency. Next, requirements to be evaluated should be considered. It is necessary to analyze a lot of typhoons, especially when obtaining the expected annual loss. The distribution of inundation depths for each return period used for underwriting and the loss for the representative return period required for risk management target at low-frequency risks, so it is not necessary to analyze all typhoons of stochastic typhoon model. In this case, if the method applied to the Tokyo Bay in this chapter is used to calculate until the hazard and loss amount in the representative return period converge, it is possible to reduce the calculation load

The author sincere thanks Professor Shigeru Tabeta and Assistant professor Katsunori Mizuno of The University of Tokyo for their considerable guidance. Special thanks are due the members of MS&AD InterRisk Research and Consulting, *Storm Surge Risk Assessment for Non-Life Insurance DOI: http://dx.doi.org/10.5772/intechopen.94157*

on reducing computational load in areas where flooding is unlikely to occur, such as the Tokyo Bay. However, when targeting areas with high flooding frequency, such as Southeast Asia, the number of typhoons that can be flooded will be huge, and the effect of reducing computational load cannot be expected. Therefore, it is necessary to judge the risk assessment by JPM-OS allowing the calculation accuracy in such areas with high flood frequency. Next, requirements to be evaluated should be considered. It is necessary to analyze a lot of typhoons, especially when obtaining the expected annual loss. The distribution of inundation depths for each return period used for underwriting and the loss for the representative return period required for risk management target at low-frequency risks, so it is not necessary to analyze all typhoons of stochastic typhoon model. In this case, if the method applied to the Tokyo Bay in this chapter is used to calculate until the hazard and loss amount in the representative return period converge, it is possible to reduce the calculation load and evaluate with high accuracy.

#### **Acknowledgements**

*Coastal Environments*

**4. Conclusion**

**Figure 6.**

On the other hand, the places where the number of floods was extremely high in the numerical calculation of storm surges are waterside areas where no assets actually exist. As a result of estimating the loss amount ignoring the loss at these areas, it was confirmed that the expected annual loss amount has converged. Therefore, if the asset amount distribution can be corrected more realistic this problem will be solved. It also suggests that asset allocation is very important for risk assessment.

*Estimated loss by rank for each number of extracted typhoons (based on Hisamatsu et al. [37] 2020).*

In this chapter, the efforts of storm surge risk assessment in the non-life insurance industry are introduced based on the author's experience. In the insurance industry, probabilistic storm surge risk assessment is required for risk management and underwriting, and a lot of analysis is required. Therefore, how to reduce the calculation load without degrading the calculation accuracy is being discussed. In this chapter, author introduced a previous study on reducing computational load. In particular, an example in which the procedure of numerically calculating only

This procedure is useful in two ways. The first point is to significantly reduce the calculation load. The procedure aims at both the reduction of the calculation load and the reduction of the calculation error compared to other methods. By applying the procedure, it was found that for Tokyo Bay, the number of typhoons to be calculated can be reduced from about 90,000 to 1000 and the calculation accuracy can be maintained. By utilizing this procedure, the cost of storm surge risk assessment can be reduced, and the reduction of insurance rate may be possible, so that taking out insurance and transferring risks to ensure a safer life for more people are expected. In addition, even if the stochastic typhoon model is updated, the risk can be quickly evaluated based on the latest knowledge considering climate change and reflected in the risk management of the insurance company. The second point is that the loss amount can be evaluated more appropriately by considering the variation of the astronomical tide level. The uncertainty associated with risk assessment for insurance purposes can be recognized and reduced by varying the astronomical

In the insurance industry, the procedure applied to the Tokyo Bay in this chapter and the method like JPM-OS should be used separately. First, the characteristics of the evaluation target site should be considered. Since the procedure applied to the Tokyo Bay analyzes storm surges for all typhoons that may flood, it has a great effect

typhoons that can cause floods was applied to Tokyo Bay is introduced.

**134**

tide level.

The author sincere thanks Professor Shigeru Tabeta and Assistant professor Katsunori Mizuno of The University of Tokyo for their considerable guidance. Special thanks are due the members of MS&AD InterRisk Research and Consulting, Inc. for their comments and supports.

#### **Conflict of interest**

The authors declare no conflict of interest.

#### **Author details**

Rikito Hisamatsu MS&AD InterRisk Research and Consulting, Inc., Tokyo, Japan

\*Address all correspondence to: rikito.hisamatsu@ms-ad-hd.com

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[13] Suzuki T. JAPAN'S INNUNDATION

[14] Tsujita D, Yasuda T, Shinohara M, Mori N, Mase H. Evaluation of aggregate

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**139**

surge inundation

**1. Introduction**

**Chapter 9**

Development of an Ocean Hazards

Classification Scheme (OHCS)

for Projecting Future Scenario

*Oceana Francis, Linqiang Yang, Harrison Togia* 

such a probabilistic risk assessment in a more localized location.

**Keywords:** ocean hazards, vulnerability ranking, Hawaii statewide highways, sea level change rate, wave height, shoreline change rate, tsunami inundation, storm

Throughout the northern and southern Pacific Oceans, lay many remote islands.

These islands are prone to extreme waves, tectonic activity, and climate change which results in storm surges, shoreline change, tsunamis, and sea level rise. The remoteness of these islands, which allows these regions to capture fully-developed seas, and their lack of a continental shelf, puts them at particular risk to ocean hazards. The Hawaiian Islands are among the most remote islands in the world. Seven

Built Infrastructure

*and Gleb Panteleev*

**Abstract**

Vulnerability Ranking on Coastal

From many sources, we develop an ocean hazard classification scheme (OHCS) based on the collection of historical and projected ocean hazards data at 302 locations along Hawaii's state coastal highways. The OHCS identifies ocean hazards impacting coastal built infrastructure, i.e. roadways. In the OHCS, we first rank the vulnerability of: sea level rise; waves; shoreline change; tsunami; and storm surge. Next, using our developed OHCS, provide the vulnerability ranking for all five variables combined. We find the highest OHCS to be on Molokai, the island that has the highest OHCS numbers for most of the island. For the majority of state highway locations in Hawaii, we find the highest vulnerability is from storm surge, with tsunami threat being the second largest contributor. Sea level rise should also be considered a contributor since higher sea levels contribute to more extreme storm surge and tsunami inundation. Although the OHCS is applied towards roads in our study, our method can be applied towards any coastal island-based built infrastructure vulnerability scheme. This is an important tool in planning for future construction projects or identifying which hazards to focus on in more detailed assessments,

#### **Chapter 9**

*Coastal Environments*

s00477-016-1377-5

[Accessed: 2020-9-23]

2020-9-23]

18. 2143-2160. 2018; DOI: https://doi. org/10.5194/nhess-18-2143-2018

Engineering). 49. 439-444. 2005; DOI: https://doi.org/10.2208/prohe.49.439

[35] Hisamatsu R, Kawabe K, Mizuno Y, Shinozuka Y. Horie K. DEVELOPMENT OF FLOOD DAMAGE FUNCTIONS BASED ON INSURANCE LOSS DUE TO 2015 KANTO–TOHOKU HEAVY RAINFALL. Journal of JSCE. Vol.7. 22-29. 2019; DOI: https://doi. org/10.2208/journalofjsce.7.1\_22

[36] Nakajo S, Mori N, Yasuda T, Mase H. Global Stochastic Tropical Cyclone Model Based on Principal Component Analysis and Cluster Analysis. J. Appl. Meteor. Climatol.. 53. 1547- 1577.2014; DOI: https://doi.org/10.1175/

[37] Hisamatsu R, Tabeta S, Kim S, Mizuno K. CONSIDERATION ON UTILITY OF STOCHASTIC STORM

SURGE RISK ASSESSMENT PROCEDURE FOR REDUCING CALCULATION LOAD. Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), vol. 76, 2020

JAMC-D-13-08.1

[29] Dias W, Edirisooriya U. Derivation of tsunami damage curves from fragility functions. Natural Hazards. 96. 1153-1166. 2019; DOI: https://doi. org/10.1007/s11069-019-03601-8

[30] Lin N, Shullman E. Dealing with hurricane surge flooding in a changing environment: part I. Risk assessment considering storm climatology change, sea level rise, and coastal development. Stochastic Environmental Research and Risk Assessment. 31. 2379-2400. 2017; DOI: https://doi.org/10.1007/

[31] Federal Emergency Management Agency. Hazus Flood Model User Guidance; Available from: https:// www.fema.gov/media-librarydata/1564766454464-d77d2c219be0 f54315aa79ac5dbc3547/Hazus\_4-2\_ Flood\_User\_Manual\_August\_2019.pdf

[32] US Army Corps of Engineers. North Atlantic Coast Comprehensive Study: Resilient Adaptation to Increasing Risk; Available from: https://api.army.mil/ e2/c/downloads/379567.pdf [Accessed:

[33] Kar B, Hodgson M E. Observational

Residential Loss from a Storm Surge. GIScience & Remote Sensing. 49. 202-227. 2012; DOI: https://doi. org/10.2747/1548-1603.49.2.202

Harada K, Okamoto M, Fukutome K, Suga M, Kawata Y. DEVELOPMENT OF FRAGILITY FUNCTION FOR WOODEN HOUSE OBTAINED FROM THE FIELD INVESTIGATION OF FLOOD DISASTER DUE TO JULY 13, 2004 NIIGATA HEAVY RAINFALL. Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic

Scale and Modeled Potential

[34] Suzuki S, Koshimura S,

**138**

## Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future Scenario Vulnerability Ranking on Coastal Built Infrastructure

*Oceana Francis, Linqiang Yang, Harrison Togia and Gleb Panteleev*

#### **Abstract**

From many sources, we develop an ocean hazard classification scheme (OHCS) based on the collection of historical and projected ocean hazards data at 302 locations along Hawaii's state coastal highways. The OHCS identifies ocean hazards impacting coastal built infrastructure, i.e. roadways. In the OHCS, we first rank the vulnerability of: sea level rise; waves; shoreline change; tsunami; and storm surge. Next, using our developed OHCS, provide the vulnerability ranking for all five variables combined. We find the highest OHCS to be on Molokai, the island that has the highest OHCS numbers for most of the island. For the majority of state highway locations in Hawaii, we find the highest vulnerability is from storm surge, with tsunami threat being the second largest contributor. Sea level rise should also be considered a contributor since higher sea levels contribute to more extreme storm surge and tsunami inundation. Although the OHCS is applied towards roads in our study, our method can be applied towards any coastal island-based built infrastructure vulnerability scheme. This is an important tool in planning for future construction projects or identifying which hazards to focus on in more detailed assessments, such a probabilistic risk assessment in a more localized location.

**Keywords:** ocean hazards, vulnerability ranking, Hawaii statewide highways, sea level change rate, wave height, shoreline change rate, tsunami inundation, storm surge inundation

#### **1. Introduction**

Throughout the northern and southern Pacific Oceans, lay many remote islands. These islands are prone to extreme waves, tectonic activity, and climate change which results in storm surges, shoreline change, tsunamis, and sea level rise. The remoteness of these islands, which allows these regions to capture fully-developed seas, and their lack of a continental shelf, puts them at particular risk to ocean hazards. The Hawaiian Islands are among the most remote islands in the world. Seven

natural phenomena have been identified as posing significant threat to coastal areas of the Hawaiian Islands which include: coastal erosion, sea level rise, major storms, volcanic and seismic activity, tsunami inundation, coastal stream flooding, and extreme seasonal high wave events [1]. Coastal slope, distance to shoreline and geologic setting are also important factors when considering coastal infrastructure exposure and vulnerability.

We consider ocean hazards on coastal infrastructure, in this case, road infrastructure. Our previous study [2] used ocean hazard values which include: historical sea level rise, historical significant wave height, tides, and historical shoreline change (without sea level rise). A methodology was developed to quantify historical ocean hazards at critical road locations that have particularly large CRESI (Coastal Road Erosion Susceptibility Index) values and where the Department of Transportation is concerned about road collapse. Note, that although tides is an important ocean variable that should correctly be considered around much of the world, we have omitted it now, in this study, since the Hawaiian Islands have a low mean tidal range of about 2 feet [3].

Here, we propose that using projected ocean hazards may give a more accurate representation when planning for future climate change on infrastructure. The ocean hazards we use include historical and projected sea level rise; projected shoreline change with sea level rise; projected storm surge; historical and projected tsunamis, and historical extreme seasonal high wave events. We develop a quantitative Ocean Hazards Classification Scheme (OHCS) based on the Ocean Hazards Database (OHD) [4] of 302 mileposts across coastal state routes in Hawaii (**Figure 1**). These

#### **Figure 1.**

*Study location area, State of Hawaii, USA. The red squares show the location on each island. The white circles (with inner black dots) indicate the milepost (MP) locations where each measurement was taken.*

**141**

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future…*

road elevation, and historical road degradation due to coastal processes.

mileposts are identified as vulnerable in [2] due to: road distance to the shoreline,

Although probability risks assessments (PRAs) are used widely to give predictions of storminess or shoreline change in a region, it is a time consuming method requiring historical data for twenty years or more, in order to create accurate projections. Also, it is often limited to a localized area due to long computational times. The aim of this study is to obtain the projected ocean hazards vulnerability rankings using projected rates and projected inundation and the CVI method [5]. The data comes from various governmental and academic sources, which are used and put into the OHCS equation to develop one number, ranging from 0 to 100. With these rankings, we are not only able to produce an overall vulnerability ranking for the five hazards, but we are also able to identify which of the five hazards

In the next section, we discuss our methodology and the development of the five ocean hazard variables (sea level rise, waves, shoreline change, tsunamis, storm surge) that we use. In Section 3, we give the results by evaluating which of the five ocean hazards most affect vulnerable highway sections in the State of Hawaii and show the overall vulnerability rankings. In the subsequent sections, Sections 4, 5, 6, 7, and 8, we give the conclusions, acknowledgements, references, figures and tables, respectively.

The use of historical and projected values is important towards the development of an Ocean Hazards Classification Scheme (OHCS) for projecting future scenario vulnerability ranking on coastal built infrastructure. Our variables we consider: (1) sea level rise rate, (2) wave height, (3) shoreline change rate, (4) tsunami

Variable (1), sea level rise, is the sea level rise rate (1905–2050, extreme scenario) (in/yr). Local sea level rise is the result of both global sea level rise and local factors. Global sea level rise is due to warmer ocean temperatures and melting land ice, both caused by climate change. Local factors include land motions and tides, currents, and winds. Local sea levels can rise faster than the average global rate.

Variable (2), maximum annually recurring waves, is the significant wave height

(2010–2018) (ft). This includes all forecasted wind-waves from 2010 to 2018, which was modeled in the wind-driven Simulating WAves Nearshore (SWAN)

Variable (3), shoreline change, is the mean projected shoreline change rate (2008–2100) (ft/yr); and CRESI – armoring ranking (1–5) [6]. Variable 3, shoreline change, determines the seaward encroachment of the beach towards the road and how protected the road is, whether there is existing armoring or not. Shoreline change is seasonal, where erosion and accretion are present during different times of the year. The most significant shoreline change is influenced by wave action, particularly storm surge events, which occur almost annually, transporting much of

Variable (4), tsunamis, considers the historical and hypothetical inundation (ft). Variable 4, tsunamis, are seismic ocean waves causing coastal inundation caused by earthquakes, underwater landslides, volcanic eruptions, or meteorites. Variable (5), storm surge, is Category 1, 2, 3, and 4 storm inundation (ft). Variable 5, storm surge, is a rapid rise in sea level causing coastal inundation due to

low pressure, high winds, and high waves associated with hurricanes.

inundation, and (5) storm surge inundation, are described here.

*DOI: http://dx.doi.org/10.5772/intechopen.94996*

most affects the coastal road section in a region.

**2. Methods and data**

**2.1 Methodology**

wave model.

the coastline away during one event.

#### *Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future… DOI: http://dx.doi.org/10.5772/intechopen.94996*

mileposts are identified as vulnerable in [2] due to: road distance to the shoreline, road elevation, and historical road degradation due to coastal processes.

Although probability risks assessments (PRAs) are used widely to give predictions of storminess or shoreline change in a region, it is a time consuming method requiring historical data for twenty years or more, in order to create accurate projections. Also, it is often limited to a localized area due to long computational times.

The aim of this study is to obtain the projected ocean hazards vulnerability rankings using projected rates and projected inundation and the CVI method [5]. The data comes from various governmental and academic sources, which are used and put into the OHCS equation to develop one number, ranging from 0 to 100. With these rankings, we are not only able to produce an overall vulnerability ranking for the five hazards, but we are also able to identify which of the five hazards most affects the coastal road section in a region.

In the next section, we discuss our methodology and the development of the five ocean hazard variables (sea level rise, waves, shoreline change, tsunamis, storm surge) that we use. In Section 3, we give the results by evaluating which of the five ocean hazards most affect vulnerable highway sections in the State of Hawaii and show the overall vulnerability rankings. In the subsequent sections, Sections 4, 5, 6, 7, and 8, we give the conclusions, acknowledgements, references, figures and tables, respectively.

#### **2. Methods and data**

#### **2.1 Methodology**

*Coastal Environments*

exposure and vulnerability.

mean tidal range of about 2 feet [3].

**140**

**Figure 1.**

*Study location area, State of Hawaii, USA. The red squares show the location on each island. The white circles* 

natural phenomena have been identified as posing significant threat to coastal areas of the Hawaiian Islands which include: coastal erosion, sea level rise, major storms, volcanic and seismic activity, tsunami inundation, coastal stream flooding, and extreme seasonal high wave events [1]. Coastal slope, distance to shoreline and geologic setting are also important factors when considering coastal infrastructure

We consider ocean hazards on coastal infrastructure, in this case, road infrastructure. Our previous study [2] used ocean hazard values which include: historical sea level rise, historical significant wave height, tides, and historical shoreline change (without sea level rise). A methodology was developed to quantify historical ocean hazards at critical road locations that have particularly large CRESI (Coastal Road Erosion Susceptibility Index) values and where the Department of Transportation is concerned about road collapse. Note, that although tides is an important ocean variable that should correctly be considered around much of the world, we have omitted it now, in this study, since the Hawaiian Islands have a low

Here, we propose that using projected ocean hazards may give a more accurate representation when planning for future climate change on infrastructure. The ocean hazards we use include historical and projected sea level rise; projected shoreline change with sea level rise; projected storm surge; historical and projected tsunamis, and historical extreme seasonal high wave events. We develop a quantitative Ocean Hazards Classification Scheme (OHCS) based on the Ocean Hazards Database (OHD) [4] of 302 mileposts across coastal state routes in Hawaii (**Figure 1**). These

*(with inner black dots) indicate the milepost (MP) locations where each measurement was taken.*

The use of historical and projected values is important towards the development of an Ocean Hazards Classification Scheme (OHCS) for projecting future scenario vulnerability ranking on coastal built infrastructure. Our variables we consider: (1) sea level rise rate, (2) wave height, (3) shoreline change rate, (4) tsunami inundation, and (5) storm surge inundation, are described here.

Variable (1), sea level rise, is the sea level rise rate (1905–2050, extreme scenario) (in/yr). Local sea level rise is the result of both global sea level rise and local factors. Global sea level rise is due to warmer ocean temperatures and melting land ice, both caused by climate change. Local factors include land motions and tides, currents, and winds. Local sea levels can rise faster than the average global rate.

Variable (2), maximum annually recurring waves, is the significant wave height (2010–2018) (ft). This includes all forecasted wind-waves from 2010 to 2018, which was modeled in the wind-driven Simulating WAves Nearshore (SWAN) wave model.

Variable (3), shoreline change, is the mean projected shoreline change rate (2008–2100) (ft/yr); and CRESI – armoring ranking (1–5) [6]. Variable 3, shoreline change, determines the seaward encroachment of the beach towards the road and how protected the road is, whether there is existing armoring or not. Shoreline change is seasonal, where erosion and accretion are present during different times of the year. The most significant shoreline change is influenced by wave action, particularly storm surge events, which occur almost annually, transporting much of the coastline away during one event.

Variable (4), tsunamis, considers the historical and hypothetical inundation (ft). Variable 4, tsunamis, are seismic ocean waves causing coastal inundation caused by earthquakes, underwater landslides, volcanic eruptions, or meteorites.

Variable (5), storm surge, is Category 1, 2, 3, and 4 storm inundation (ft). Variable 5, storm surge, is a rapid rise in sea level causing coastal inundation due to low pressure, high winds, and high waves associated with hurricanes.

#### *Coastal Environments*

Using the ranking, from 1 to 5, for each of the five variables, we input these variables into one equation (Eq. (1)), which we call the Ocean Hazard Classification Scheme (OHCS), to obtain a value between 1 to 100, where the higher the values, the more vulnerable the region.

$$OHCS = \sqrt{\frac{\left(Varianble\ \mathbf{1} \ast Valable\ \mathbf{2} \ast Variable\ \mathbf{3} \ast Variable\ \mathbf{4} \ast Variable\ \mathbf{5}\right)^{\mathbf{1}\ast \mathbf{3}}}{\mathbf{5}}} \tag{1}$$

where *Variable* 1 is 2050 sea level rise rate ranking (extreme scenario) (1 to 5), *Variable* 2 is significant wave height ranking (1 to 5), *Variable* 3 is mean shoreline change rate ranking (1 to 5), *Variable* 4 is tsunami inundation ranking (1 to 5), and *Variable* 5 is storm surge inundation ranking (1 to 5).

Eq. (1) is taken as the square root of the geometric mean of the ranking variables, with the addition of a power scalar to adjust the range of theoretical OHCS rankings to maximize at a value of 100. Therefore as the number of variables change, so does the scalar power. When considering five input variables, each with a maximum ranked value of 5, a power scalar value of 1.345 results in a potential maximum OHCS value of 100. Our method is similar to that used in Chapter 1 of [2] for calculating Coastal Road Erosion Susceptibility Index (CRESI) values, in [7, 8] who was the first to use the coastal vulnerability index (CVI) for the entire Hawaiian Islands to assess coastal vulnerability, and that described by [5] for finding the coastal vulnerability index (CVI) rankings.

#### **2.2 Sea level rise**

Historical rates of sea level rise are estimated from observed data, and future sea level rise rates are estimated from projected data. For both historical and future scenarios, it is essential to take the spatial variation into consideration when determining the rate of sea level rise. For this reason, we divide each island into a certain number of segments and derive the historical and future sea level rise rates for each segment, respectively. Currently, there are two types of data used to estimate the historical sea level rise rate: tide gauge and satellite altimetry data. Tide gauges are usually placed on piers and measure the sea level relative to a nearby geodetic benchmark, known as relative sea level (RSL). Satellite altimetry measures the sea level relative to a reference ellipsoid, known as absolute sea level (ASL). Here, we study how the sea level rise affects the coastal infrastructure (i.e. roads) in the Hawaiian Islands. Therefore, we focus on the trend estimates of RSL. There are six tide gauge stations in operation in the Hawaiian Islands: NAWI is located in Nawiliwili Bay, Kauai Island with data spanning 1955–2016; MOKU is located in Mokuoloe Island, Oahu Island with data spanning 1957–2016; HONO is located in Honolulu, Oahu Island with data spanning 1905–2016; KAHA is located in Kahului Harbor, Maui Island with data spanning 1947–2016; KAWA is located in Kawaihae, Hawaii Island with data spanning 1988–2016; and HIHA is located in Hilo, Hawaii Island with data spanning 1927–2016. The RSL data of the six available stations in the Hawaiian Islands are downloaded from the Permanent Service for Mean Sea Level (PSMSL) [9, 10]. We make use of all available RSL data from the six tide gauge stations to estimate the RSL trends, respectively. Before estimating the RSL trends, the following process is applied. First, the seasonal signal is removed from the RSL time series using the Seasonal Trend Decomposition using Loess (STL) procedure [11]. Second, we remove the common-mode-oceanographic signals from each RSL time series. The common-mode-oceanographic signals can be derived by averaging the monthly detrended and de-seasoned RSL

**143**

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future…*

time series of the all six available tide-gauge stations in the Hawaiian Islands. Finally, the linear trends of the RSL are estimated. However, tide gauge stations are sparsely distributed and not all the segments are covered. For those segments not covered by the tide gauge stations, an indirect way is applied to derive the relative sea level rise trend (RSLT). The RSL variation is comprised of two components: ASL variation and vertical land motion (VLM). Eq. (2) indicates the

where *ASLT* represents ASL trend, *RSLT* represents RSL trend, and *VLMR* represents VLM rate. Therefore, the RSLT of the segments without tide gauge stations can be estimated by combining the ASLT and VLMR. In this paper, we use the reprocessed and merged-gridded sea-level-anomaly heights for global areas processed by Ssalto/Duacs [12] to derive the ASLT. The satellite altimetry data spans 1993–2017 and has a resolution of 0.25 arc degrees. If there is more than one satellite altimetry grid point near the study segment, the time series are averaged to derive the ASLT. Before estimating the ASLT, the Dynamic Atmospheric Correction (DAC) is downloaded and added back to the satellite altimetry data to keep in accordance with the tide gauge data which do not use the barometric pressure correction. The DAC data are produced by Collecte Localisation Satellites (CLS) using the Mog2D model from Legos and distributed by Aviso+, with support from CNES (https://www.aviso.altimetry.fr/). The satellite altimetry data is accessible at the Copernicus Marine Environment Monitoring Service (CMEMS) (http://marine.copernicus.eu/). The data of Global Navigation Satellite System (GNSS) which has proven to be a robust tool to monitor VLM [13–15] is used to derive the VLMR. The GNSS data is available at the Nevada Geodetic Laboratory (NGL) (http://geodesy.unr.edu/NGLStationPages/GlobalStationList) [16].

Detailed information for the selected tide gauge, satellite altimetry, and GNSS data

After deriving the historical and future sea level rise rates, we rank them according to the percentile of the observed maximum rates, respectively. If a value falls within the highest 80 to 100th percentile, it is ranked 5 (very high). Similarly, values falling within the 60 to 80th percentile are ranked 4 (high), 40 to 60th percentile are ranked 3 (moderate), 20 to 40th percentile are ranked 2 (low), and 0

Several future sea level rise scenario products have been developed to help planning and decision-making stakeholders analyze and understand vulnerabilities and future risks under scientific uncertainty. We use [17, 18] to estimate the future sea level rise rate for each segment. Sea levels under different scenarios of [17, 18] are projected to tide gauge stations and grid points, which have a resolution of 1 arc degree. If a tide gauge station exists in the segment, we use the data projected to the tide gauge station. If no tide gauge station exists in the segment, the projected grid points nearby the segment will be used. If there is more than one grid point nearby a segment, the mean value is derived and used to represent the projected sea level rise of the segment. Detailed information on the projected sea level rise data for each segment is available in [14]. In this paper, we consider the projected sea level rise under extreme scenario for 2050. For segments with tide gauge stations, the tide gauge data are integrated with the projected sea level rise data to obtain the future sea level rise rate. For segments without tide gauge stations, the combined satellite altimetry and GNSS data are integrated with the projected sea level rise data to

*ASLT RSLT VLMR* = + (2)

*DOI: http://dx.doi.org/10.5772/intechopen.94996*

relationship of the three components:

of each segment is available in [14].

obtain the future sea level rise rate.

to 20th percentile are ranked 1 (very low).

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future… DOI: http://dx.doi.org/10.5772/intechopen.94996*

time series of the all six available tide-gauge stations in the Hawaiian Islands. Finally, the linear trends of the RSL are estimated. However, tide gauge stations are sparsely distributed and not all the segments are covered. For those segments not covered by the tide gauge stations, an indirect way is applied to derive the relative sea level rise trend (RSLT). The RSL variation is comprised of two components: ASL variation and vertical land motion (VLM). Eq. (2) indicates the relationship of the three components:

$$\text{ASLT} = \text{RSL} \, T + \text{VLMR} \tag{2}$$

where *ASLT* represents ASL trend, *RSLT* represents RSL trend, and *VLMR* represents VLM rate. Therefore, the RSLT of the segments without tide gauge stations can be estimated by combining the ASLT and VLMR. In this paper, we use the reprocessed and merged-gridded sea-level-anomaly heights for global areas processed by Ssalto/Duacs [12] to derive the ASLT. The satellite altimetry data spans 1993–2017 and has a resolution of 0.25 arc degrees. If there is more than one satellite altimetry grid point near the study segment, the time series are averaged to derive the ASLT. Before estimating the ASLT, the Dynamic Atmospheric Correction (DAC) is downloaded and added back to the satellite altimetry data to keep in accordance with the tide gauge data which do not use the barometric pressure correction. The DAC data are produced by Collecte Localisation Satellites (CLS) using the Mog2D model from Legos and distributed by Aviso+, with support from CNES (https://www.aviso.altimetry.fr/). The satellite altimetry data is accessible at the Copernicus Marine Environment Monitoring Service (CMEMS) (http://marine.copernicus.eu/). The data of Global Navigation Satellite System (GNSS) which has proven to be a robust tool to monitor VLM [13–15] is used to derive the VLMR. The GNSS data is available at the Nevada Geodetic Laboratory (NGL) (http://geodesy.unr.edu/NGLStationPages/GlobalStationList) [16]. Detailed information for the selected tide gauge, satellite altimetry, and GNSS data of each segment is available in [14].

Several future sea level rise scenario products have been developed to help planning and decision-making stakeholders analyze and understand vulnerabilities and future risks under scientific uncertainty. We use [17, 18] to estimate the future sea level rise rate for each segment. Sea levels under different scenarios of [17, 18] are projected to tide gauge stations and grid points, which have a resolution of 1 arc degree. If a tide gauge station exists in the segment, we use the data projected to the tide gauge station. If no tide gauge station exists in the segment, the projected grid points nearby the segment will be used. If there is more than one grid point nearby a segment, the mean value is derived and used to represent the projected sea level rise of the segment. Detailed information on the projected sea level rise data for each segment is available in [14]. In this paper, we consider the projected sea level rise under extreme scenario for 2050. For segments with tide gauge stations, the tide gauge data are integrated with the projected sea level rise data to obtain the future sea level rise rate. For segments without tide gauge stations, the combined satellite altimetry and GNSS data are integrated with the projected sea level rise data to obtain the future sea level rise rate.

After deriving the historical and future sea level rise rates, we rank them according to the percentile of the observed maximum rates, respectively. If a value falls within the highest 80 to 100th percentile, it is ranked 5 (very high). Similarly, values falling within the 60 to 80th percentile are ranked 4 (high), 40 to 60th percentile are ranked 3 (moderate), 20 to 40th percentile are ranked 2 (low), and 0 to 20th percentile are ranked 1 (very low).

*Coastal Environments*

**2.2 Sea level rise**

the more vulnerable the region.

*Variable* 5 is storm surge inundation ranking (1 to 5).

ing the coastal vulnerability index (CVI) rankings.

Using the ranking, from 1 to 5, for each of the five variables, we input these variables into one equation (Eq. (1)), which we call the Ocean Hazard Classification Scheme (OHCS), to obtain a value between 1 to 100, where the higher the values,

( )

where *Variable* 1 is 2050 sea level rise rate ranking (extreme scenario) (1 to 5), *Variable* 2 is significant wave height ranking (1 to 5), *Variable* 3 is mean shoreline change rate ranking (1 to 5), *Variable* 4 is tsunami inundation ranking (1 to 5), and

Eq. (1) is taken as the square root of the geometric mean of the ranking variables, with the addition of a power scalar to adjust the range of theoretical OHCS rankings to maximize at a value of 100. Therefore as the number of variables change, so does the scalar power. When considering five input variables, each with a maximum ranked value of 5, a power scalar value of 1.345 results in a potential maximum OHCS value of 100. Our method is similar to that used in Chapter 1 of [2] for calculating Coastal Road Erosion Susceptibility Index (CRESI) values, in [7, 8] who was the first to use the coastal vulnerability index (CVI) for the entire Hawaiian Islands to assess coastal vulnerability, and that described by [5] for find-

Historical rates of sea level rise are estimated from observed data, and future sea level rise rates are estimated from projected data. For both historical and future scenarios, it is essential to take the spatial variation into consideration when determining the rate of sea level rise. For this reason, we divide each island into a certain number of segments and derive the historical and future sea level rise rates for each segment, respectively. Currently, there are two types of data used to estimate the historical sea level rise rate: tide gauge and satellite altimetry data. Tide gauges are usually placed on piers and measure the sea level relative to a nearby geodetic benchmark, known as relative sea level (RSL). Satellite altimetry measures the sea level relative to a reference ellipsoid, known as absolute sea level (ASL). Here, we study how the sea level rise affects the coastal infrastructure (i.e. roads) in the Hawaiian Islands. Therefore, we focus on the trend estimates of RSL. There are six tide gauge stations in operation in the Hawaiian Islands: NAWI is located in Nawiliwili Bay, Kauai Island with data spanning 1955–2016; MOKU is located in Mokuoloe Island, Oahu Island with data spanning 1957–2016; HONO is located in Honolulu, Oahu Island with data spanning 1905–2016; KAHA is located in Kahului Harbor, Maui Island with data spanning 1947–2016; KAWA is located in Kawaihae, Hawaii Island with data spanning 1988–2016; and HIHA is located in Hilo, Hawaii Island with data spanning 1927–2016. The RSL data of the six available stations in the Hawaiian Islands are downloaded from the Permanent Service for Mean Sea Level (PSMSL) [9, 10]. We make use of all available RSL data from the six tide gauge stations to estimate the RSL trends, respectively. Before estimating the RSL trends, the following process is applied. First, the seasonal signal is removed from the RSL time series using the Seasonal Trend Decomposition using Loess (STL) procedure [11]. Second, we remove the common-mode-oceanographic signals from each RSL time series. The common-mode-oceanographic signals can be derived by averaging the monthly detrended and de-seasoned RSL

12345 5 *Variable Variable Variable Variable Variable OHCS* \*\*\* \* <sup>=</sup> (1)

1.345

**142**

#### **2.3 Maximum annually recurring waves**

Due to the sparse distribution of buoy stations in the Hawaiian Islands region, there is not enough coverage to provide wave information at a local level, i.e., for each milepost. Therefore, we use modeled wave output downloaded from Pacific Islands Ocean Observing System (PacIOOS) [19] to understand the wave conditions at each milepost. PacIOOS provides 5-day hourly wave forecasts that are calibrated using local wave buoys for the Hawaiian Islands region. Wave forecasts are simulated using WaveWatch III (WW3), surrounding the main Hawaiian Islands at an approximate resolution of 0.05 degrees, and the SWAN model, surrounding each main island at an approximate resolution of 0.31 mile (500 m) [19]. In this study, we use the wave forecasts simulated by the SWAN model, which has a finer resolution. The time span of wave data for each island varies, i.e., Oahu: 2010–2019, Maui: 2016–2019, Molokai: 2016–2019, Kauai: 2010–2019, Hawaii: 2016–2019. For each milepost, a 'virtual buoy', that is, the closest point offshore and perpendicular to the road at each milepost, is selected to obtain wave data. In this study, significant wave height was used, which is estimated as four times the square root to the zeroth order moment of the wave spectrum [19].

We extract the maximum annually recurring wave information using the method presented in [20, 21]. The process of deriving maximum annually recurring wave information is as follows. First, we identify the local peaks from the time series of significant wave heights with a time interval greater than 24 hours. Second, the peaks are divided into different bins according to incoming directions. Here, we select a 30-degree bin window, which shifts by 15-degree increments. Therefore, a maximum of 24 bins can be obtained, and there are overlaps between bins. Third, we select the three highest significant wave heights from each year and perform the generalized extreme value (GEV) fit for each bin. Then, the maximum annually recurring significant wave height (MARSWH) for each bin are derived. Finally, the wave information triplet with maximum MARSWH among all bins is selected as the annually recurring maximum wave information. We repeat this process to obtain wave information at each milepost.

After deriving the wave information triplet for each milepost, we rank the two index variables, MARSWH and corresponding peak period, according to the percentile of the observed maximum value, respectively. If a value falls within the highest 80 to 100th percentile, it is ranked 5 (very high). Similarly, values falling within the 60 to 80th percentile is ranked 4 (high), 40 to 60th percentile is ranked 3 (moderate), 20 to 40th percentile is ranked 2 (low), and 0 to 20th percentile is ranked 1 (very low).

#### **2.4 Shoreline change**

Erosion and weakening shorelines are a direct threat to coastal roads and infrastructure. Through the course of this study, we have observed both damages and an increased failure potential of nearshore state roads induced by coastal erosion.

Seasonal and storm-driven shifts in the directional transportation of sand, as well as the projected effects of sea level rise (SLR), limit the long-term numerical modeling of Hawaiian shoreline evolution. To assess the potential impact of an acceleration of shoreline change in response to rising sea levels, we interpret relative rates of shoreline change from erosion exposure forecasts developed by [20]. In [22], they describe the probabilistic method by which erosion exposure areas are determined. In [22], they use an equation for shoreline change similar to that of [23], while substituting in the geometric sediment transport model for shoreline equilibrium proposed by [24], to forecast the evolution of sandy shores

**145**

**2.5 Tsunamis**

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future…*

on the islands of Oahu, Maui, and Kauai. Hindcast and study area limits for the model in [20] are identified from historical shorelines produced by [25]. Hindcast timespans vary between islands and study areas. Complete hindcast timespans for each island are: 1910–2007 on Oahu, 1899–2007 on Maui, and 1926–2008 on Kauai [25]. Acceleration of SLR used by [22] are taken from the Intergovernmental Panel on Climate Change (IPCC) 2013 report, AR5 high-end representative concentration

Shoreline change is shown in ArcGIS by digitizing the nearshore vegetation line over different periods [20]. Digitized vegetation lines (polylines), which we refer to as "Shoreline Vegetation Lines (SVLs)", are determined in [20] as the 80th percentile of the probability density function for change due to SLR of the present SVL defined during a 2006–2008 study. Projected shoreline change rates (ft/yr) are determined by dividing the length between the SVLs at the milepost, from the present vegetation line to future projected vegetation lines for SLR of 0.5, 1.1, 2.0, and 3.2 feet, by the number of years within the respective period. We assess the shoreline change at each milepost along a new polyline perpendicular to the road and extending through the SVLs, which we identify as the "measurement axis". Projected occurrence for SLR of 0.5, 1.1, 2.0, and 3.2 feet is identified by [20] using the IPCC 2013 report AR5 RCP 8.5 scenario, for the years 2030, 2050, 2075, and 2100, respectively [26]. We average the rates of shoreline erosion and accretion at

pathway (RCP) 8.5 scenario – the "business as usual" scenario [26].

each milepost over the four time periods (i.e. 2030, 2050, 2075, 2100).

Rates of interpreted averaged shoreline change are ranked into five classes according to their percentile ranges, from no change and accretion to the maximum observed averaged rate. Erosion values roughly within the highest 80 to100th percentile, are ranked 5 (very high). Similarly, erosion rates falling near the 60 to 80th percentile are ranked 4 (high), the 40 to 60th percentile is ranked 3 (moderate), and the 20 to 40th percentile is ranked 2 (low). Shoreline change values representing accretion or no change, fall roughly within the 0 to 20th percentile of maximum observed values are ranked 1 (very low). Mileposts outside of [20] are ranked based on armoring observations made in CRESI [6]. Mileposts with shoreline change values of N/A and hard armoring, where the CRESI armor ranking is greater than 3, are ranked 2 (low). Mileposts with shoreline change values of N/A and no armoring, where CRESI armor ranking [6] is less than or equal to 3, are ranked 3 (moderate).

Tsunami, which is commonly caused by an earthquake in subduction zones, is one of the most devastating coastal hazards. The Hawaiian Islands region, located in the center of the Pacific Ocean, is circled by the 'Ring of Fire', a region of

subduction zone volcanism. Therefore, the Hawaiian Islands region is significantly threatened by tsunamis, which result from earthquakes along the 'Ring of Fire' [27, 28]. For this reason, we take into account tsunami hazard in our assessment. We use modeled tsunami flow depth data, provided by [29], to create inundation for each milepost which in turn helps us understand how tsunami hazards affect the coastal roads in the Hawaiian Islands. The term tsunami flow depth refers to the height of tsunami water surface above ground, which can be derived by subtracting ground elevation from tsunami water level. In this study, we use two types of tsunami flow depth data: one is modeled according to historical earthquake events, and the other is based on hypothetical earthquake events. Both types of data were simulated using the model Non-hydrostatic Evolution of Ocean Wave (NEOWAVE), which is a community model developed and maintained at the University of Hawaii [30, 31]. Historical tsunami scenarios are based on the five most destructive far-field or trans-Pacific tsunamis, which were generated by the

*DOI: http://dx.doi.org/10.5772/intechopen.94996*

#### *Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future… DOI: http://dx.doi.org/10.5772/intechopen.94996*

on the islands of Oahu, Maui, and Kauai. Hindcast and study area limits for the model in [20] are identified from historical shorelines produced by [25]. Hindcast timespans vary between islands and study areas. Complete hindcast timespans for each island are: 1910–2007 on Oahu, 1899–2007 on Maui, and 1926–2008 on Kauai [25]. Acceleration of SLR used by [22] are taken from the Intergovernmental Panel on Climate Change (IPCC) 2013 report, AR5 high-end representative concentration pathway (RCP) 8.5 scenario – the "business as usual" scenario [26].

Shoreline change is shown in ArcGIS by digitizing the nearshore vegetation line over different periods [20]. Digitized vegetation lines (polylines), which we refer to as "Shoreline Vegetation Lines (SVLs)", are determined in [20] as the 80th percentile of the probability density function for change due to SLR of the present SVL defined during a 2006–2008 study. Projected shoreline change rates (ft/yr) are determined by dividing the length between the SVLs at the milepost, from the present vegetation line to future projected vegetation lines for SLR of 0.5, 1.1, 2.0, and 3.2 feet, by the number of years within the respective period. We assess the shoreline change at each milepost along a new polyline perpendicular to the road and extending through the SVLs, which we identify as the "measurement axis". Projected occurrence for SLR of 0.5, 1.1, 2.0, and 3.2 feet is identified by [20] using the IPCC 2013 report AR5 RCP 8.5 scenario, for the years 2030, 2050, 2075, and 2100, respectively [26]. We average the rates of shoreline erosion and accretion at each milepost over the four time periods (i.e. 2030, 2050, 2075, 2100).

Rates of interpreted averaged shoreline change are ranked into five classes according to their percentile ranges, from no change and accretion to the maximum observed averaged rate. Erosion values roughly within the highest 80 to100th percentile, are ranked 5 (very high). Similarly, erosion rates falling near the 60 to 80th percentile are ranked 4 (high), the 40 to 60th percentile is ranked 3 (moderate), and the 20 to 40th percentile is ranked 2 (low). Shoreline change values representing accretion or no change, fall roughly within the 0 to 20th percentile of maximum observed values are ranked 1 (very low). Mileposts outside of [20] are ranked based on armoring observations made in CRESI [6]. Mileposts with shoreline change values of N/A and hard armoring, where the CRESI armor ranking is greater than 3, are ranked 2 (low). Mileposts with shoreline change values of N/A and no armoring, where CRESI armor ranking [6] is less than or equal to 3, are ranked 3 (moderate).

#### **2.5 Tsunamis**

*Coastal Environments*

**2.3 Maximum annually recurring waves**

moment of the wave spectrum [19].

wave information at each milepost.

Due to the sparse distribution of buoy stations in the Hawaiian Islands region, there is not enough coverage to provide wave information at a local level, i.e., for each milepost. Therefore, we use modeled wave output downloaded from Pacific Islands Ocean Observing System (PacIOOS) [19] to understand the wave conditions at each milepost. PacIOOS provides 5-day hourly wave forecasts that are calibrated using local wave buoys for the Hawaiian Islands region. Wave forecasts are simulated using WaveWatch III (WW3), surrounding the main Hawaiian Islands at an approximate resolution of 0.05 degrees, and the SWAN model, surrounding each main island at an approximate resolution of 0.31 mile (500 m) [19]. In this study, we use the wave forecasts simulated by the SWAN model, which has a finer resolution. The time span of wave data for each island varies, i.e., Oahu: 2010–2019, Maui: 2016–2019, Molokai: 2016–2019, Kauai: 2010–2019, Hawaii: 2016–2019. For each milepost, a 'virtual buoy', that is, the closest point offshore and perpendicular to the road at each milepost, is selected to obtain wave data. In this study, significant wave height was used, which is estimated as four times the square root to the zeroth order

We extract the maximum annually recurring wave information using the method presented in [20, 21]. The process of deriving maximum annually recurring wave information is as follows. First, we identify the local peaks from the time series of significant wave heights with a time interval greater than 24 hours. Second, the peaks are divided into different bins according to incoming directions. Here, we select a 30-degree bin window, which shifts by 15-degree increments. Therefore, a maximum of 24 bins can be obtained, and there are overlaps between bins. Third, we select the three highest significant wave heights from each year and perform the generalized extreme value (GEV) fit for each bin. Then, the maximum annually recurring significant wave height (MARSWH) for each bin are derived. Finally, the wave information triplet with maximum MARSWH among all bins is selected as the annually recurring maximum wave information. We repeat this process to obtain

After deriving the wave information triplet for each milepost, we rank the two index variables, MARSWH and corresponding peak period, according to the percentile of the observed maximum value, respectively. If a value falls within the highest 80 to 100th percentile, it is ranked 5 (very high). Similarly, values falling within the 60 to 80th percentile is ranked 4 (high), 40 to 60th percentile is ranked 3 (moderate), 20 to 40th percentile is ranked 2 (low), and 0 to 20th percentile is ranked 1

Erosion and weakening shorelines are a direct threat to coastal roads and infrastructure. Through the course of this study, we have observed both damages and an increased failure potential of nearshore state roads induced by coastal erosion. Seasonal and storm-driven shifts in the directional transportation of sand, as well as the projected effects of sea level rise (SLR), limit the long-term numerical modeling of Hawaiian shoreline evolution. To assess the potential impact of an acceleration of shoreline change in response to rising sea levels, we interpret relative rates of shoreline change from erosion exposure forecasts developed by [20]. In [22], they describe the probabilistic method by which erosion exposure areas are determined. In [22], they use an equation for shoreline change similar to that of [23], while substituting in the geometric sediment transport model for shoreline equilibrium proposed by [24], to forecast the evolution of sandy shores

**144**

(very low).

**2.4 Shoreline change**

Tsunami, which is commonly caused by an earthquake in subduction zones, is one of the most devastating coastal hazards. The Hawaiian Islands region, located in the center of the Pacific Ocean, is circled by the 'Ring of Fire', a region of subduction zone volcanism. Therefore, the Hawaiian Islands region is significantly threatened by tsunamis, which result from earthquakes along the 'Ring of Fire' [27, 28]. For this reason, we take into account tsunami hazard in our assessment. We use modeled tsunami flow depth data, provided by [29], to create inundation for each milepost which in turn helps us understand how tsunami hazards affect the coastal roads in the Hawaiian Islands. The term tsunami flow depth refers to the height of tsunami water surface above ground, which can be derived by subtracting ground elevation from tsunami water level. In this study, we use two types of tsunami flow depth data: one is modeled according to historical earthquake events, and the other is based on hypothetical earthquake events. Both types of data were simulated using the model Non-hydrostatic Evolution of Ocean Wave (NEOWAVE), which is a community model developed and maintained at the University of Hawaii [30, 31]. Historical tsunami scenarios are based on the five most destructive far-field or trans-Pacific tsunamis, which were generated by the

1946 Aleutian, the 1952 Kamchatkan, the 1957 Aleutian, the 1960 Chilean, and the 1964 Alaskan earthquakes. NEOWAVE model parameters are calibrated by comparing results with well-documented runup records for those tsunamis on Hawaii shores [32–35]. The NEOWAVE model applied nested grids with increasing resolution, from 2 arcminutes (~2.3 miles) for open ocean to 0.3 arcseconds (~29.53 ft) for coastlines [32–35]. Hypothetical tsunami scenarios are based on two extreme tsunamis which apply the seismic source parameters of two hypothetical great Aleutian earthquakes. Tectonic parameters of the two great Aleutian earthquakes, with moment magnitudes of (Mw) 9.3 and 9.6, are compiled by NOAA Pacific Marine Environmental Laboratory (PMEL) and both hypothetical earthquakes are identified by a seismological study as potential sources of devastating tsunamis to Hawaii [27–29]. The model also applies nested grids with increasing resolution from 2 arcminutes (~2.3 miles) for open ocean to 0.3 arcseconds (~29.53 ft) for coastlines [29].

We use the Geographical Information System (GIS) software ArcGIS to create tsunami inundation and extract tsunami flow depth values for each milepost. Tsunami flow depths are ranked for each milepost as follows. First, mileposts are classified into three categories: Category 1 has values in the historical scenario, Category 2 has no values in the historical scenario, but has values in the hypothetical scenario, and Category 3 has no values in both historical and hypothetical scenarios. For Category 1, if a value falls within the highest 67 to 100th percentile of the observed maximum value in the historical scenario, it is ranked 5 (very high). Similarly, if a value falls within the 33 to 67th percentile, it is ranked 4 (high), and within the 0 to 33rd percentile, it is ranked 3 (moderate). For Category 2, because the tsunami flow depth in the hypothetical scenario for milepost 6 (MP 6) on Route 83, North Shore, Oahu exceeds three standard deviations of the mean, we rank it 2 and remove it from the list when searching the maximum value of Category 2. Therefore, if a value falls within the highest 50 to 100th percentile of the observed maximum value in the hypothetical scenario, it is ranked 2 (low), within the 0 to 50th percentile, it is ranked 1 (very low). All mileposts in Category 3 are ranked 1 (very low).

#### **2.6 Storm surge**

Predicting and preparing for hurricanes is a top priority for the residents and city managers of Hawaii. To assess the "worst case scenario" of inundation from storm surge, we utilize the most recent national storm surge hazard maps produced by the Storm Surge Unit (SSU) of the National Hurricane Center (NHC), National Oceanic and Atmospheric Administration (NOAA) [36].

Version 1 of the national storm surge hazard maps are published by [36] and include inundation model results for flooding caused by storm surge along the East and Gulf Coasts of the United States. Version 2, also by [36], became available in November 2018 and includes storm surge inundation estimates for the U.S. Virgin Islands, Hawaii, and Hispaniola. Measures of storm surge inundation height reflect the extents of flooding caused by storm driven uplift of the ocean surface. Estimates of storm surge inundation in this assessment are based on GIS datasets obtained through personal communication with members of the SSU and NOAA affiliates. Internal SSU issues, beyond the control of our team, have prevented a complete handover and description of the Hawaii storm surge data. As a result of the incomplete handover, there are minor errors in the projection of the data, as well as a limited understanding of the model hindcast. However, despite the shortcomings, the data still remains the best and most complete storm surge inundation data for the Hawaiian Islands. Storm surge hazard data presents hypothetical inundations

**147**

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future…*

found using a composite deterministic and probabilistic approach with the Sea, Lake and Overland Surges from Hurricanes (SLOSH) numerical model, developed by the National Weather Service (NWS). In the Hawaiian Islands, where steep offshore bathymetry can produce an increase in mean water level due to wave dissipation, or wave setup, the SLOSH model is loosely coupled to the third generation of the SWAN model to account for storm-related increases in mean water levels. SLOSH model forecasts consider historical atmospheric and hurricane track data, to produce a model of the wind field which drives hypothetical storm surge. However, as we mention, internal SSU issues prevents us from describing the time period for the historical atmospheric data, as well as the number and distribution of historical

Hawaii SLOSH model estimates include inundation scenarios for category 1 through 4 hurricanes and a broad range of storm tracks and landfall locations, consisting of hundreds of thousands of hypothetical hurricanes. Assessed storm surge inundation heights are determined as the maximum of the maximum envelops of water (MOMs), relative to a DEM of Hawaii from NOAA Office for Coastal Management (OCM) high-resolution raster elevation datasets. DEMs for each island are reoriented and divided to optimize SLOSH operation, resulting in polar oriented cells of various sizes, as small as roughly 24 ft (9 m), on each side. Within each cell, MOM values are determined in feet as a combination of all simulated inundation scenarios, with the MOM identifying the greatest observed inundation height from all simulations. Milepost assessments of storm surge inundation are sampled from the individual category of storm surge datasets, within a circular buffer centered on the milepost with a radius of 82 ft (25 m). Ranked values of storm surge inundation are determined as the percent coverage-area-weighted mean of the MOM values within the milepost buffer area. Percent coverage for each milepost buffer area is determined by first using the ArcGIS zonal statistics tool to find the buffer area overlapping with the storm surge dataset. Then, the inundation, or overlapping of the buffer area, is divided by the known total buffer area of roughly 21,000 square ft, to determine the percentage of the buffer inundated. Mean inundation height within the milepost buffer areas is also determined using the ArcGIS zonal statistics tool. Ranked values of storm surge inundation are finally calculated

as the mean inundation height multiplied by the percent coverage.

50th percentile for Category 4 storm surge are ranked 1 (very low).

**3. Results: projected vulnerability for coastal highways**

Percent coverage-area-weighted mean storm surge inundation heights are ranked based on their observed distribution within the maximum observed value of each category of storm, respectively. Mileposts with inundation heights within the 50 to 100th percentile of Category 1 storm surge are ranked 5 (very high). Inundation heights greater than zero and within the 0 to 50th percentile of Category 1 storm surge, as well as the 50 to 100th percentile of Category 2 storm surge, are ranked 4 (high). If milepost inundation heights for Category 2 storm surge are greater than zero and within the 0 to 50th percentile, or within the 50 to 100th percentile for Category 3 storm surge, they are ranked 3 (moderate). Storm surge inundation heights within the 50 to 100th percentile for Category 4 storm surge, or within the 0 to 50th percentile for Category 3 storm surge are ranked 2 (low). Milepost assessments with no inundation, or with inundation heights in the 0 to

There are twelve regions in the State of Hawaii where coastal roads, are owned by the State, and selected due to their location to shoreline, elevation and road condition from previous ocean hazards. Of these twelve regions, four are on Oahu, two

*DOI: http://dx.doi.org/10.5772/intechopen.94996*

storm tracks.

#### *Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future… DOI: http://dx.doi.org/10.5772/intechopen.94996*

found using a composite deterministic and probabilistic approach with the Sea, Lake and Overland Surges from Hurricanes (SLOSH) numerical model, developed by the National Weather Service (NWS). In the Hawaiian Islands, where steep offshore bathymetry can produce an increase in mean water level due to wave dissipation, or wave setup, the SLOSH model is loosely coupled to the third generation of the SWAN model to account for storm-related increases in mean water levels. SLOSH model forecasts consider historical atmospheric and hurricane track data, to produce a model of the wind field which drives hypothetical storm surge. However, as we mention, internal SSU issues prevents us from describing the time period for the historical atmospheric data, as well as the number and distribution of historical storm tracks.

Hawaii SLOSH model estimates include inundation scenarios for category 1 through 4 hurricanes and a broad range of storm tracks and landfall locations, consisting of hundreds of thousands of hypothetical hurricanes. Assessed storm surge inundation heights are determined as the maximum of the maximum envelops of water (MOMs), relative to a DEM of Hawaii from NOAA Office for Coastal Management (OCM) high-resolution raster elevation datasets. DEMs for each island are reoriented and divided to optimize SLOSH operation, resulting in polar oriented cells of various sizes, as small as roughly 24 ft (9 m), on each side. Within each cell, MOM values are determined in feet as a combination of all simulated inundation scenarios, with the MOM identifying the greatest observed inundation height from all simulations. Milepost assessments of storm surge inundation are sampled from the individual category of storm surge datasets, within a circular buffer centered on the milepost with a radius of 82 ft (25 m). Ranked values of storm surge inundation are determined as the percent coverage-area-weighted mean of the MOM values within the milepost buffer area. Percent coverage for each milepost buffer area is determined by first using the ArcGIS zonal statistics tool to find the buffer area overlapping with the storm surge dataset. Then, the inundation, or overlapping of the buffer area, is divided by the known total buffer area of roughly 21,000 square ft, to determine the percentage of the buffer inundated. Mean inundation height within the milepost buffer areas is also determined using the ArcGIS zonal statistics tool. Ranked values of storm surge inundation are finally calculated as the mean inundation height multiplied by the percent coverage.

Percent coverage-area-weighted mean storm surge inundation heights are ranked based on their observed distribution within the maximum observed value of each category of storm, respectively. Mileposts with inundation heights within the 50 to 100th percentile of Category 1 storm surge are ranked 5 (very high). Inundation heights greater than zero and within the 0 to 50th percentile of Category 1 storm surge, as well as the 50 to 100th percentile of Category 2 storm surge, are ranked 4 (high). If milepost inundation heights for Category 2 storm surge are greater than zero and within the 0 to 50th percentile, or within the 50 to 100th percentile for Category 3 storm surge, they are ranked 3 (moderate). Storm surge inundation heights within the 50 to 100th percentile for Category 4 storm surge, or within the 0 to 50th percentile for Category 3 storm surge are ranked 2 (low). Milepost assessments with no inundation, or with inundation heights in the 0 to 50th percentile for Category 4 storm surge are ranked 1 (very low).

#### **3. Results: projected vulnerability for coastal highways**

There are twelve regions in the State of Hawaii where coastal roads, are owned by the State, and selected due to their location to shoreline, elevation and road condition from previous ocean hazards. Of these twelve regions, four are on Oahu, two

*Coastal Environments*

coastlines [29].

(very low).

**2.6 Storm surge**

1946 Aleutian, the 1952 Kamchatkan, the 1957 Aleutian, the 1960 Chilean, and the 1964 Alaskan earthquakes. NEOWAVE model parameters are calibrated by comparing results with well-documented runup records for those tsunamis on Hawaii shores [32–35]. The NEOWAVE model applied nested grids with increasing resolution, from 2 arcminutes (~2.3 miles) for open ocean to 0.3 arcseconds (~29.53 ft) for coastlines [32–35]. Hypothetical tsunami scenarios are based on two extreme tsunamis which apply the seismic source parameters of two hypothetical great Aleutian earthquakes. Tectonic parameters of the two great Aleutian earthquakes, with moment magnitudes of (Mw) 9.3 and 9.6, are compiled by NOAA Pacific Marine Environmental Laboratory (PMEL) and both hypothetical earthquakes are identified by a seismological study as potential sources of devastating tsunamis to Hawaii [27–29]. The model also applies nested grids with increasing resolution from 2 arcminutes (~2.3 miles) for open ocean to 0.3 arcseconds (~29.53 ft) for

We use the Geographical Information System (GIS) software ArcGIS to create tsunami inundation and extract tsunami flow depth values for each milepost. Tsunami flow depths are ranked for each milepost as follows. First, mileposts are classified into three categories: Category 1 has values in the historical scenario, Category 2 has no values in the historical scenario, but has values in the hypothetical scenario, and Category 3 has no values in both historical and hypothetical scenarios. For Category 1, if a value falls within the highest 67 to 100th percentile of the observed maximum value in the historical scenario, it is ranked 5 (very high). Similarly, if a value falls within the 33 to 67th percentile, it is ranked 4 (high), and within the 0 to 33rd percentile, it is ranked 3 (moderate). For Category 2, because the tsunami flow depth in the hypothetical scenario for milepost 6 (MP 6) on Route 83, North Shore, Oahu exceeds three standard deviations of the mean, we rank it 2 and remove it from the list when searching the maximum value of Category 2. Therefore, if a value falls within the highest 50 to 100th percentile of the observed maximum value in the hypothetical scenario, it is ranked 2 (low), within the 0 to 50th percentile, it is ranked 1 (very low). All mileposts in Category 3 are ranked 1

Predicting and preparing for hurricanes is a top priority for the residents and city managers of Hawaii. To assess the "worst case scenario" of inundation from storm surge, we utilize the most recent national storm surge hazard maps produced by the Storm Surge Unit (SSU) of the National Hurricane Center (NHC), National

Version 1 of the national storm surge hazard maps are published by [36] and include inundation model results for flooding caused by storm surge along the East and Gulf Coasts of the United States. Version 2, also by [36], became available in November 2018 and includes storm surge inundation estimates for the U.S. Virgin Islands, Hawaii, and Hispaniola. Measures of storm surge inundation height reflect the extents of flooding caused by storm driven uplift of the ocean surface. Estimates of storm surge inundation in this assessment are based on GIS datasets obtained through personal communication with members of the SSU and NOAA affiliates. Internal SSU issues, beyond the control of our team, have prevented a complete handover and description of the Hawaii storm surge data. As a result of the incomplete handover, there are minor errors in the projection of the data, as well as a limited understanding of the model hindcast. However, despite the shortcomings, the data still remains the best and most complete storm surge inundation data for the Hawaiian Islands. Storm surge hazard data presents hypothetical inundations

Oceanic and Atmospheric Administration (NOAA) [36].

**146**

are on Molokai, three are on Maui, three are on Kauai, and one is on Hawaii. Oahu includes Waianae Coast (WC), North Shore (NS), East Shore (ES), and East Oahu (EO). Molokai includes Molokai West (KW) and Molokai East (KE). Maui includes West Maui (WM), East Maui (EM), and Central Maui (CM). Kauai includes West Kauai (W), North Kauai (N), and East Kauai (E). Hawaii includes Hilo (HILO).

Here, we present our results and how five ocean hazards: sea level rise, waves, shoreline change, tsunami, and storm surge are collectively used to rank the vulnerability of coastal highways in the State of Hawaii. A list of ocean hazards data and their associated references (superscripted) used for the Ocean Hazards Classification Scheme (OHCS) in Eq. (1) is shown in **Table 1**.

**Table 2** is the Ocean Hazards Classification Scheme (OHCS) for historical and projected ocean hazards developed from the Ocean Hazards Database (OHD) [4] for state coastal roads in the State of Hawaii. In the first column is the vulnerability rank, 1 to 5, where 1 is low vulnerability and 5 is high vulnerability. The remaining columns are the associated Variables and their resulting rates, heights or depths according to the methodology described in Section 2, using 302 mileposts across the State from [2]. Using **Table 2**, we rank each Variable (1 to 5) and apply it to Eq. (1) to retrieve the OHCS ranking, that is, a combined ranking of vulnerability for sea level rise, significant wave height, shoreline change, tsunami and storm surge. Our results are listed as follows.

Oahu Waianae Coast (WC), **Figures 2-4**: Includes 39 mileposts. The OHCS vulnerability ranking ranges from 1 to 3, with a few higher ranking outliers of 5, 5, and 6 at MPs 19 + 0.55, 16 + 0.41 and 10 + 0.25, respectively. In this region, sea level rise ranges from 1 to 2, significant wave height ranges from 1 to 2, shoreline change ranges from 2 to 3, tsunami ranges from 1 to 4, and storm surge ranges from 1 to 4. The outliers, i.e. the OHCS rankings of 5 and 6 at MP 19 + 0.55, 16 + 0.41 and 10 + 0.25, are a result from the increased tsunami and storm surge rankings, due to proximity and elevation of the road to the shoreline at those particular locations.

Oahu North Shore (NS), **Figure 5**: Includes 19 mileposts. The OHCS vulnerability ranking ranges from 2 to 15. In this region, sea level rise is 2, significant wave height ranges from 1 to 3, shoreline change ranges from 2 to 3, tsunami ranges from 1 to 4, and storm surge is 1 with a three MPs ranked at 4. Although most of the OHCS values range from 7 or below, the three MPs worth noting, i.e. MP 3 + 0.66, 4 + 0.49 and 6, with a ranking of 15, 15, and 10, respectively, are the MPs with a storm surge ranking of 4, compared to the other MPs with a storm surge ranking of 1.


#### **Table 1.**

*Historical and projected ocean hazards variables used in the Ocean Hazards Classification Scheme (OHCS) for State coastal roads in the State of Hawaii. For more detailed explanation of each, refer to [4]. 12 inches = 1 foot = 0.3048 meters.*

**149**

**Table 2.**

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future…*

**Maximum Annually Recurring Waves**

**Significant Wave Height [19, 20] (2010–2018)**

1 <0.1 in/yr <7 ft <0 ft/yr No

7 to 14 ft 0 to 2 ft/

14 to 21 ft 2 to 5 ft/yr

5 > 0.4 in/yr > 29 ft > 7 ft/yr Historical

*for State coastal roads in the State of Hawaii. 12 inches = 1 foot = 0.3048 meters.*

yr & "N/A" with >3 Armoring Ranking

& "N/A" with ≤3 Armoring Ranking

21 to 29 ft 5 to 7 ft/yr Historical

**Variable 1 Variable 2 Variable 3 Variable 4 Variable 5**

**Tsunami Storm Surge**

**Weighted Mean Storm Surge Inundation [36] (Hypothetical)**

No Inundation or Category 4 Inundation <4 ft

Category 3 Inundation <4 ft or Category 4 Inundation of 4 to 8 ft

Category 3 Inundation of 4 to 7 ft or Category 2 Inundation <1 ft

Category 2 Inundation of 1 to 6 ft or Category 1 Inundation <1 ft

Category 1 Inundation of 1 to 4 ft

**Tsunami Inundation [29, 32–35] (Historical and Hypothetical)**

inundation or Hypothetical inundation <16 ft with no Historical Inundation

Hypothetical inundation ≥16 ft with no Historical Inundation

Historical inundation <6 ft

inundation of 6 to 12 ft

> inundation ≥12 ft

**Shoreline Change**

**Mean Shoreline Change Rate [6, 20] (2008– 2100)**

Oahu East Shore (ES), **Figures 6-8**: Includes 44 mileposts. The OHCS vulnerability ranking ranges from 1 to 12. In this region, sea level rise is 2, significant wave height ranges from 1 to 2, shoreline change ranges from 1 to 3, tsunami ranges from 1 to 4, and storm surge ranges from 1 to 5. We see particularly high OHCS rankings of 9 to 12 at certain MPs. These regions with OHCS values of 9 to 12, is a result from

*Ocean Hazards Classification Scheme (OHCS) for historical and projected ocean hazards developed from [34]* 

Oahu East Oahu (EO), **Figures 9** and **10**: Includes 20 mileposts. The OHCS vulnerability ranking ranges from 1 to 10. In this region, sea level rise ranges from 1 to 2, significant wave height ranges from 1 to 2, shoreline change ranges from 2 to 3, tsunami ranges from 1 to 4, and storm surge ranges from 1 to 5. High OHCS rankings

of 7 to 10, is a result from the increased tsunami and storm surge rankings.

the increased tsunami and storm surge rankings.

*DOI: http://dx.doi.org/10.5772/intechopen.94996*

**Sea Level Rise**

**2050 Sea Level Rise Rate [9, 10, 12, 14, 17, 18] (1905– 2050, extreme scenario)**

2 0.1 to 0.2

3 0.2 to 0.3

4 0.3 to 0.4

in/yr

in/yr

in/yr

**Vunerability Rank**


*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future… DOI: http://dx.doi.org/10.5772/intechopen.94996*

#### **Table 2.**

*Coastal Environments*

results are listed as follows.

surge ranking of 1.

2 Maximum Annually

Recurring Waves

are on Molokai, three are on Maui, three are on Kauai, and one is on Hawaii. Oahu includes Waianae Coast (WC), North Shore (NS), East Shore (ES), and East Oahu (EO). Molokai includes Molokai West (KW) and Molokai East (KE). Maui includes West Maui (WM), East Maui (EM), and Central Maui (CM). Kauai includes West Kauai (W), North Kauai (N), and East Kauai (E). Hawaii includes Hilo (HILO). Here, we present our results and how five ocean hazards: sea level rise, waves,

shoreline change, tsunami, and storm surge are collectively used to rank the vulnerability of coastal highways in the State of Hawaii. A list of ocean hazards data and their associated references (superscripted) used for the Ocean Hazards

**Table 2** is the Ocean Hazards Classification Scheme (OHCS) for historical and projected ocean hazards developed from the Ocean Hazards Database (OHD) [4] for state coastal roads in the State of Hawaii. In the first column is the vulnerability rank, 1 to 5, where 1 is low vulnerability and 5 is high vulnerability. The remaining columns are the associated Variables and their resulting rates, heights or depths according to the methodology described in Section 2, using 302 mileposts across the State from [2]. Using **Table 2**, we rank each Variable (1 to 5) and apply it to Eq. (1) to retrieve the OHCS ranking, that is, a combined ranking of vulnerability for sea level rise, significant wave height, shoreline change, tsunami and storm surge. Our

Oahu Waianae Coast (WC), **Figures 2-4**: Includes 39 mileposts. The OHCS vulnerability ranking ranges from 1 to 3, with a few higher ranking outliers of 5, 5, and 6 at MPs 19 + 0.55, 16 + 0.41 and 10 + 0.25, respectively. In this region, sea level rise ranges from 1 to 2, significant wave height ranges from 1 to 2, shoreline change ranges from 2 to 3, tsunami ranges from 1 to 4, and storm surge ranges from 1 to 4. The outliers, i.e. the OHCS rankings of 5 and 6 at MP 19 + 0.55, 16 + 0.41 and 10 + 0.25, are a result from the increased tsunami and storm surge rankings, due to proximity and elevation of the road to the shoreline at those particular locations. Oahu North Shore (NS), **Figure 5**: Includes 19 mileposts. The OHCS vulnerability ranking ranges from 2 to 15. In this region, sea level rise is 2, significant wave height ranges from 1 to 3, shoreline change ranges from 2 to 3, tsunami ranges from 1 to 4, and storm surge is 1 with a three MPs ranked at 4. Although most of the OHCS values range from 7 or below, the three MPs worth noting, i.e. MP 3 + 0.66, 4 + 0.49 and 6, with a ranking of 15, 15, and 10, respectively, are the MPs with a storm surge ranking of 4, compared to the other MPs with a storm

1 Sea Level Rise 2050 Sea Level Rise Rate [9, 10, 12, 14, 17, 18] (1905–2050,

3 Shoreline Change Mean Shoreline Change Rate [6, 20] (2008–2100) [ft/yr] 4 Tsunami Inundation Depth (Historical and Hypothetical)

*Historical and projected ocean hazards variables used in the Ocean Hazards Classification Scheme (OHCS) for State coastal roads in the State of Hawaii. For more detailed explanation of each, refer to [4]. 12 inches = 1* 

[29, 32–35] [ft] 5 Storm Surge Category 1–4 Storm Inundation Depth [36] (Hypothetical) [ft]

extreme scenario) [in/yr]

Significant Wave Height [19, 20] (2010–2018) [ft]

Classification Scheme (OHCS) in Eq. (1) is shown in **Table 1**.

**Variable Classification Description [units]**

**148**

**Table 1.**

*foot = 0.3048 meters.*

*Ocean Hazards Classification Scheme (OHCS) for historical and projected ocean hazards developed from [34] for State coastal roads in the State of Hawaii. 12 inches = 1 foot = 0.3048 meters.*

Oahu East Shore (ES), **Figures 6-8**: Includes 44 mileposts. The OHCS vulnerability ranking ranges from 1 to 12. In this region, sea level rise is 2, significant wave height ranges from 1 to 2, shoreline change ranges from 1 to 3, tsunami ranges from 1 to 4, and storm surge ranges from 1 to 5. We see particularly high OHCS rankings of 9 to 12 at certain MPs. These regions with OHCS values of 9 to 12, is a result from the increased tsunami and storm surge rankings.

Oahu East Oahu (EO), **Figures 9** and **10**: Includes 20 mileposts. The OHCS vulnerability ranking ranges from 1 to 10. In this region, sea level rise ranges from 1 to 2, significant wave height ranges from 1 to 2, shoreline change ranges from 2 to 3, tsunami ranges from 1 to 4, and storm surge ranges from 1 to 5. High OHCS rankings of 7 to 10, is a result from the increased tsunami and storm surge rankings.

#### **Figure 2.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu Waianae Coast (WC) MP 13 + 0.1 to 19 + 0.55. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

#### **Figure 3.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu Waianae Coast (WC) MP 7 + 0.67\_13 + 0.1. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

Molokai Molokai West (KW), **Figure 11**: Includes 5 mileposts. The OHCS vulnerability ranking ranges from 13 to 17, with a low OHCS ranking outlier of 3 at MP 2. In this region for OHCS rankings of 13 to 17, the sea level rise is 5, significant wave height is 1, shoreline change ranges from 2 to 3, tsunami is 3, and storm surge

**151**

**Figure 5.**

*theoretical maximum of 100.*

**Figure 4.**

*theoretical maximum of 100.*

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future…*

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu Waianae Coast (WC) MP 3 to 7 + 0.67. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a* 

ranges from 4 to 5. High OHCS rankings of 13 to 17 is a result of higher rankings for

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu North Shore (NS) MP 2 to 10 + 0.58. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a* 

Molokai Molokai East (KE), **Figures 12-14**: Includes 49 mileposts. The OHCS vulnerability ranking ranges from 3 to 33. In this region, the sea level rise is 5, significant wave height ranges from 1 to 2, shoreline change is 3, tsunami ranges from

sea level rise, storm surge and tsunami inundation in this region.

*DOI: http://dx.doi.org/10.5772/intechopen.94996*

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future… DOI: http://dx.doi.org/10.5772/intechopen.94996*

#### **Figure 4.**

*Coastal Environments*

**Figure 2.**

*from 1 to a theoretical maximum of 100.*

*from 1 to a theoretical maximum of 100.*

**150**

**Figure 3.**

Molokai Molokai West (KW), **Figure 11**: Includes 5 mileposts. The OHCS vulnerability ranking ranges from 13 to 17, with a low OHCS ranking outlier of 3 at MP 2. In this region for OHCS rankings of 13 to 17, the sea level rise is 5, significant wave height is 1, shoreline change ranges from 2 to 3, tsunami is 3, and storm surge

*wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase* 

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu Waianae Coast (WC) MP 7 + 0.67\_13 + 0.1. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant* 

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu Waianae Coast (WC) MP 13 + 0.1 to 19 + 0.55. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase* 

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu Waianae Coast (WC) MP 3 to 7 + 0.67. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

#### **Figure 5.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu North Shore (NS) MP 2 to 10 + 0.58. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

ranges from 4 to 5. High OHCS rankings of 13 to 17 is a result of higher rankings for sea level rise, storm surge and tsunami inundation in this region.

Molokai Molokai East (KE), **Figures 12-14**: Includes 49 mileposts. The OHCS vulnerability ranking ranges from 3 to 33. In this region, the sea level rise is 5, significant wave height ranges from 1 to 2, shoreline change is 3, tsunami ranges from

#### **Figure 6.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu East Shore (ES) MP 32 to 38. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

#### **Figure 7.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu East Shore (ES) MP 23 to 32. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

1 to 5, and storm surge ranges from 1 to 5. High OHCS rankings is a result of high rankings for sea level rise, storm surge and tsunami inundation in this region.

Maui West Maui (WM), **Figures 15-17**: Includes 48 mileposts. The OHCS vulnerability ranking ranges from 1 to 14. In this region, the sea level rise is 2, significant wave height ranges from 1 to 2, shoreline change ranges from 1 to 3, tsunami

**153**

**Figure 9.**

*theoretical maximum of 100.*

**Figure 8.**

*maximum of 100.*

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future…*

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu East Shore (ES) MP 17 to 23. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical* 

ranges from 1 to 5, and storm surge ranges from 1 to 5. High OHCS rankings is a result of high rankings for storm surge and tsunami inundation in this region.

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu East Oahu (EO) MP 9 to 17 + 0.18. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a* 

Maui East Maui (EM), **Figure 18**: Includes 11 mileposts. The OHCS vulnerability ranking ranges from 6 to 10. In this region, the sea level rise is 2, significant wave height is 1, shoreline change ranges from 2 to 3, tsunami ranges from 4 to 5, and

*DOI: http://dx.doi.org/10.5772/intechopen.94996*

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future… DOI: http://dx.doi.org/10.5772/intechopen.94996*

#### **Figure 8.**

*Coastal Environments*

**Figure 6.**

*maximum of 100.*

**152**

**Figure 7.**

*maximum of 100.*

1 to 5, and storm surge ranges from 1 to 5. High OHCS rankings is a result of high rankings for sea level rise, storm surge and tsunami inundation in this region. Maui West Maui (WM), **Figures 15-17**: Includes 48 mileposts. The OHCS vulnerability ranking ranges from 1 to 14. In this region, the sea level rise is 2, significant wave height ranges from 1 to 2, shoreline change ranges from 1 to 3, tsunami

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu East Shore (ES) MP 23 to 32. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical* 

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu East Shore (ES) MP 32 to 38. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical* 

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu East Shore (ES) MP 17 to 23. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

#### **Figure 9.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu East Oahu (EO) MP 9 to 17 + 0.18. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

ranges from 1 to 5, and storm surge ranges from 1 to 5. High OHCS rankings is a result of high rankings for storm surge and tsunami inundation in this region.

Maui East Maui (EM), **Figure 18**: Includes 11 mileposts. The OHCS vulnerability ranking ranges from 6 to 10. In this region, the sea level rise is 2, significant wave height is 1, shoreline change ranges from 2 to 3, tsunami ranges from 4 to 5, and

#### **Figure 10.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu East Oahu (EO) MP 4 to 9. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

#### **Figure 11.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Molokai Molokai West (KW) MP 2 to East 4. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

storm surge ranges from 2 to 5. High OHCS rankings is a result of high rankings for storm surge and tsunami inundation in this region.

Maui Central Maui (CM), **Figures 19** and **20**: Includes 13 mileposts. The OHCS vulnerability ranking ranges from 3 to 16. In this region, the sea level rise ranges from 2 to 5, significant wave height ranges from 1 to 5, shoreline change ranges from 2 to 5, tsunami ranges from 1 to 5, and storm surge ranges from 1 to 5. High OHCS rankings is generally a result of high rankings for sea level rise, storm surge and tsunami inundation in this region. However, significant wave height contributes to high OHCS rankings at MPs 8 + 0.42 and 8 + 0.63 and shoreline change at MP 0 + 0.05.

**155**

in this region.

*from 1 to a theoretical maximum of 100.*

**Figure 13.**

**Figure 12.**

*from 1 to a theoretical maximum of 100.*

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future…*

*Ocean Hazards Classification Scheme (OHCS) ranking for Molokai Molokai East (KE) MP 17 to 21 + 0.32. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant* 

*wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase* 

Kauai West Kauai (W), **Figure 21**: Includes 11 mileposts. The OHCS vulnerability ranking ranges from 4 to 11, with a low OHCS outlier of 1 at MP 24 + 0.91. In this region, the sea level rise is 2, significant wave height is 1, shoreline change ranges from 1 to 4, tsunami ranges from 3 to 4, and storm surge ranges from 1 to 5. High OHCS rankings is a result of high rankings for storm surge and tsunami inundation

*Ocean Hazards Classification Scheme (OHCS) ranking for Molokai Molokai East (KE) MP 10 + 0.06 to 17. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase* 

Kauai North Kauai (N), **Figure 22**: Includes 8 mileposts. The OHCS vulnerability ranking ranges from 3 to 11. In this region, the sea level rise is 2, significant wave

*DOI: http://dx.doi.org/10.5772/intechopen.94996*

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future… DOI: http://dx.doi.org/10.5772/intechopen.94996*

#### **Figure 12.**

*Coastal Environments*

**Figure 10.**

*maximum of 100.*

**154**

0 + 0.05.

**Figure 11.**

*theoretical maximum of 100.*

storm surge ranges from 2 to 5. High OHCS rankings is a result of high rankings for

*Ocean Hazards Classification Scheme (OHCS) ranking for Molokai Molokai West (KW) MP 2 to East 4. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a* 

*Ocean Hazards Classification Scheme (OHCS) ranking for Oahu East Oahu (EO) MP 4 to 9. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical* 

Maui Central Maui (CM), **Figures 19** and **20**: Includes 13 mileposts. The OHCS vulnerability ranking ranges from 3 to 16. In this region, the sea level rise ranges from 2 to 5, significant wave height ranges from 1 to 5, shoreline change ranges from 2 to 5, tsunami ranges from 1 to 5, and storm surge ranges from 1 to 5. High OHCS rankings is generally a result of high rankings for sea level rise, storm surge and tsunami inundation in this region. However, significant wave height contributes to high OHCS rankings at MPs 8 + 0.42 and 8 + 0.63 and shoreline change at MP

storm surge and tsunami inundation in this region.

*Ocean Hazards Classification Scheme (OHCS) ranking for Molokai Molokai East (KE) MP 17 to 21 + 0.32. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

#### **Figure 13.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Molokai Molokai East (KE) MP 10 + 0.06 to 17. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

Kauai West Kauai (W), **Figure 21**: Includes 11 mileposts. The OHCS vulnerability ranking ranges from 4 to 11, with a low OHCS outlier of 1 at MP 24 + 0.91. In this region, the sea level rise is 2, significant wave height is 1, shoreline change ranges from 1 to 4, tsunami ranges from 3 to 4, and storm surge ranges from 1 to 5. High OHCS rankings is a result of high rankings for storm surge and tsunami inundation in this region.

Kauai North Kauai (N), **Figure 22**: Includes 8 mileposts. The OHCS vulnerability ranking ranges from 3 to 11. In this region, the sea level rise is 2, significant wave

#### **Figure 14.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Molokai Molokai East (KE) MP 4 to 10 + 0.06. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

#### **Figure 15.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Maui West Maui (WM) MP 20 to 29. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

height ranges from 1 to 2, shoreline change ranges from 2 to 5, tsunami ranges from 3 to 4, and storm surge ranges from 1 to 5. High OHCS rankings is a result of higher rankings for storm surge and shoreline change in this region.

Kauai East Kauai (E), **Figure 23**: Includes 13 mileposts. The OHCS vulnerability ranking ranges from 2 to 9. In this region, the sea level rise is 2, significant wave

**157**

**Figure 17.**

*maximum of 100.*

**Figure 16.**

*maximum of 100.*

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future…*

*Ocean Hazards Classification Scheme (OHCS) ranking for Maui West Maui (WM) MP 15 to 20. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical* 

height ranges from 1 to 2, shoreline change ranges from 2 to 3, tsunami ranges from 1 to 4, and storm surge ranges from 1 to 5. High OHCS rankings is a result of higher

*Ocean Hazards Classification Scheme (OHCS) ranking for Maui West Maui (WM) MP 9 to 15. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical* 

Hawaii Hilo (HILO), **Figure 24**: Includes 12 mileposts. The OHCS vulnerability ranking ranges from 4 to 18. In this region, the sea level rise is 4, significant

rankings for shoreline change, tsunami and storm surge in this region.

*DOI: http://dx.doi.org/10.5772/intechopen.94996*

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future… DOI: http://dx.doi.org/10.5772/intechopen.94996*

#### **Figure 16.**

*Coastal Environments*

**Figure 14.**

*theoretical maximum of 100.*

**156**

**Figure 15.**

*maximum of 100.*

height ranges from 1 to 2, shoreline change ranges from 2 to 5, tsunami ranges from 3 to 4, and storm surge ranges from 1 to 5. High OHCS rankings is a result of higher

*Ocean Hazards Classification Scheme (OHCS) ranking for Maui West Maui (WM) MP 20 to 29. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical* 

*Ocean Hazards Classification Scheme (OHCS) ranking for Molokai Molokai East (KE) MP 4 to 10 + 0.06. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a* 

Kauai East Kauai (E), **Figure 23**: Includes 13 mileposts. The OHCS vulnerability ranking ranges from 2 to 9. In this region, the sea level rise is 2, significant wave

rankings for storm surge and shoreline change in this region.

*Ocean Hazards Classification Scheme (OHCS) ranking for Maui West Maui (WM) MP 15 to 20. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

#### **Figure 17.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Maui West Maui (WM) MP 9 to 15. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

height ranges from 1 to 2, shoreline change ranges from 2 to 3, tsunami ranges from 1 to 4, and storm surge ranges from 1 to 5. High OHCS rankings is a result of higher rankings for shoreline change, tsunami and storm surge in this region.

Hawaii Hilo (HILO), **Figure 24**: Includes 12 mileposts. The OHCS vulnerability ranking ranges from 4 to 18. In this region, the sea level rise is 4, significant

#### **Figure 18.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Maui East Maui (EM) MP 1 to 3 + 0.14. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

#### **Figure 19.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Maui Central Maui (CM) MP 6 to 9. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

wave height ranges from 1 to 2, shoreline change ranges from 2 to 3, tsunami ranges from 1 to 5, and storm surge ranges from 1 to 5. High OHCS rankings is a result of higher rankings for sea level rise, tsunami and storm surge in this region.

In summary from our results, sea level rise ranges from 1 to 5, waves ranges from 1 to 5, and the OHCS ranges from 1 to 33. Although the OHCS equation allows

**159**

**Figure 21.**

*maximum of 100.*

**Figure 20.**

*theoretical maximum of 100.*

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future…*

*Ocean Hazards Classification Scheme (OHCS) ranking for Maui Central Maui (CM) MP 0 to 0 + 0.71. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a* 

a value up to 100, OHCS only went up to 33, showing that no locations have all Variables at high vulnerability (i.e. 5), but rather a one or two Variables may be at

*Ocean Hazards Classification Scheme (OHCS) ranking for Kauai West Kauai (W) MP 24 to 28. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical* 

Another result shows that the island of Molokai has the highest OHCS overall. The Variables that contribute to the high OHCS includes sea level rise, tsunami and

rank 5 while the other Variables remain low (i.e. 1 or 2).

storm surge, all of which were nearly ranked at 5.

*DOI: http://dx.doi.org/10.5772/intechopen.94996*

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future… DOI: http://dx.doi.org/10.5772/intechopen.94996*

#### **Figure 20.**

*Coastal Environments*

**Figure 18.**

*theoretical maximum of 100.*

**158**

this region.

*maximum of 100.*

**Figure 19.**

wave height ranges from 1 to 2, shoreline change ranges from 2 to 3, tsunami ranges from 1 to 5, and storm surge ranges from 1 to 5. High OHCS rankings is a result of higher rankings for sea level rise, tsunami and storm surge in

*Ocean Hazards Classification Scheme (OHCS) ranking for Maui Central Maui (CM) MP 6 to 9. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical* 

*Ocean Hazards Classification Scheme (OHCS) ranking for Maui East Maui (EM) MP 1 to 3 + 0.14. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a* 

In summary from our results, sea level rise ranges from 1 to 5, waves ranges from 1 to 5, and the OHCS ranges from 1 to 33. Although the OHCS equation allows *Ocean Hazards Classification Scheme (OHCS) ranking for Maui Central Maui (CM) MP 0 to 0 + 0.71. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

#### **Figure 21.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Kauai West Kauai (W) MP 24 to 28. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

a value up to 100, OHCS only went up to 33, showing that no locations have all Variables at high vulnerability (i.e. 5), but rather a one or two Variables may be at rank 5 while the other Variables remain low (i.e. 1 or 2).

Another result shows that the island of Molokai has the highest OHCS overall. The Variables that contribute to the high OHCS includes sea level rise, tsunami and storm surge, all of which were nearly ranked at 5.

#### **Figure 22.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Kauai North Kauai (N) MP 2 + 0.5 to 4 + 0.51. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

#### **Figure 23.**

*Ocean Hazards Classification Scheme (OHCS) ranking for Kauai East Kauai (E) MP 5 to 11. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

A third result is that the Variable, storm surge, is consistently the largest contributor in coastal vulnerability on state roads for all islands. This is shown in the ranking of all Variables which largely show a storm surge of rank 5 at most locations, where the other Variables remain at 1 or 2. Tsunamis are the second largest contributor in our results. Although sea level rise was not one of the highest

**161**

**4. Conclusion**

**Figure 24.**

*maximum of 100.*

included also.

their region.

change the CVI (or OHCS).

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future…*

contributors, it should be considered a main contributor since the sea level rise

*Ocean Hazards Classification Scheme (OHCS) ranking for Hawaii Hilo (HILO) MP 0 to 5. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical* 

The high rankings of storm surge inundation and tsunami inundation are due to lower road elevation, which puts the road at greater risk. Road relocation inland is recommended, if possible. Where road relocation is not possible, and usually not an option for state roads in Hawaii, elevating the road infrastructure (and therefore other surrounding infrastructure) should be taken into consideration in community planning and development. To reinforce the elevated road, hardening should be

Although our Variables we consider: (1) sea level rise, (2) waves, (3) shoreline change, (4) tsunamis, and (5) storm surge, work for our study region, i.e. the Hawaiian Islands, one should be aware that assessing vulnerability is "location specific". This means that natural hazards affecting an area depend on many factors such as the geology, oceanic, bathymetric, and climate trends in a location. These factors differ region to region. Each coastal region should develop their own vulnerability ranking method to include or not include Variables which most likely affect

While natural hazard exposure to infrastructure is important, other multiple indicators should also be considered. For roadways this may include traffic volume, population served, accessibility, connectivity, reliability, land use, and roadway connection to critical infrastructures, such as hospitals and police stations [37]. However, this type of data changes frequently as land use develops at a rapid pace or additionally roads may be added. Also adding these additional indicators may

inundation amplifies storm surge and tsunami inundation.

*DOI: http://dx.doi.org/10.5772/intechopen.94996*

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future… DOI: http://dx.doi.org/10.5772/intechopen.94996*

#### **Figure 24.**

*Coastal Environments*

**Figure 22.**

*theoretical maximum of 100.*

**160**

**Figure 23.**

*maximum of 100.*

A third result is that the Variable, storm surge, is consistently the largest contributor in coastal vulnerability on state roads for all islands. This is shown in the ranking of all Variables which largely show a storm surge of rank 5 at most locations, where the other Variables remain at 1 or 2. Tsunamis are the second largest contributor in our results. Although sea level rise was not one of the highest

*Ocean Hazards Classification Scheme (OHCS) ranking for Kauai East Kauai (E) MP 5 to 11. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical* 

*Ocean Hazards Classification Scheme (OHCS) ranking for Kauai North Kauai (N) MP 2 + 0.5 to 4 + 0.51. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a* 

*Ocean Hazards Classification Scheme (OHCS) ranking for Hawaii Hilo (HILO) MP 0 to 5. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.*

contributors, it should be considered a main contributor since the sea level rise inundation amplifies storm surge and tsunami inundation.

#### **4. Conclusion**

The high rankings of storm surge inundation and tsunami inundation are due to lower road elevation, which puts the road at greater risk. Road relocation inland is recommended, if possible. Where road relocation is not possible, and usually not an option for state roads in Hawaii, elevating the road infrastructure (and therefore other surrounding infrastructure) should be taken into consideration in community planning and development. To reinforce the elevated road, hardening should be included also.

Although our Variables we consider: (1) sea level rise, (2) waves, (3) shoreline change, (4) tsunamis, and (5) storm surge, work for our study region, i.e. the Hawaiian Islands, one should be aware that assessing vulnerability is "location specific". This means that natural hazards affecting an area depend on many factors such as the geology, oceanic, bathymetric, and climate trends in a location. These factors differ region to region. Each coastal region should develop their own vulnerability ranking method to include or not include Variables which most likely affect their region.

While natural hazard exposure to infrastructure is important, other multiple indicators should also be considered. For roadways this may include traffic volume, population served, accessibility, connectivity, reliability, land use, and roadway connection to critical infrastructures, such as hospitals and police stations [37]. However, this type of data changes frequently as land use develops at a rapid pace or additionally roads may be added. Also adding these additional indicators may change the CVI (or OHCS).

#### *Coastal Environments*

Coastal hazard and risk not only comes in the form of the physical processes on the ecosystem or built infrastructure, but also through social perceptions, as well. Perceptions of coastal hazards and risks and community support for engineered adaptation methods are important for implementation among different stakeholder groups (experts, businesses, and community members) [38].

By understanding the vulnerability of a region, we may assign what adaptation method to use in vulnerable coastal regions dealing with climate change, in particular, inundation. These engineered adaptation methods include offshore barriers, coastal armoring, elevated development, floating development, floodable development, living shorelines, and managed retreat [39]. In the future, if we want to continue to live on coast, we must adapt.

#### **Acknowledgements**

Funding provided by the State of Hawaii Department of Transportation Highways Division, under Project Number HWY-06-16. We would like to also acknowledge our affiliations to the following at the University of Hawaii at Manoa: Department of Civil and Environmental Engineering, Sea Grant College Program, and the Coastal Hydraulics Engineering Resilience (CHER) Lab. Publications fees provided by Research & Training Revolving Fund from the Civil and Environmental Engineering Department at the University of Hawaii at Manoa.

#### **Author details**

Oceana Francis1 \*, Linqiang Yang1 , Harrison Togia1 and Gleb Panteleev2

1 University of Hawaii at Manoa, Honolulu, Hawaii, USA

2 Naval Research Laboratory, Stennis Space Center, Mississippi, USA

\*Address all correspondence to: oceanaf@hawaii.edu

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**163**

frr3fsx3j6.2.

*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future…*

[7] Onat, Y., Marchant, M., Francis, O., and Kim, K. (2018). Coastal exposure of the Hawaiian Islands using GIS-based index modeling. Ocean & Coastal Management, 163, 113-129, ISSN 0964-5691, https://doi.org/10.1016/j.

ocecoaman.2018.06.003.

ocecoaman.2018.02.021.

data/obtaining/.

[9] Holgate, S.J., Matthews, A., Woodworth, P.L., Rickards, L.J., Tamisiea, M.E., Bradshaw, E., et al. (2013). New Data Systems and Products at the Permanent Service for Mean Sea Level. Journal of Coastal Research , 29(3), 493- 504. https://doi.org/10.2112/ JCOASTRES-D-12-00175.1.

[10] PSMSL. (2018). Tide Gauge Data. Retrieved from https://www.psmsl.org/

[11] Cleveland, R.B., Cleveland, W.S., McRae, J.E., and Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, Vol. 6, pp. 3-73.

[12] Mertz, F., Pujol, M.-I., and Faugère,

Y. (2018). Product user manual (Version 4.0). Copernicus Marine Environment Monitoring Service. Retrieved from http://cmems-resources. cls.fr/documents/PUM/CMEMS-SL-

[13] Wöppelmann, G., and Marcos, M. (2016). Vertical land motion as a key to understanding sea level change and variability. Reviews of Geophysics, 54(1), 64-92. https://doi.

PUM-008-032-051.pdf.

org/10.1002/2015RG000502

[8] Onat, Y., Francis, O., and Kim, K. (2018). Vulnerability assessment and adaptation to sea level rise in high-wave environments: A case study on O'ahu, Hawai'i, Ocean & Coastal Management, 157, 147-159, ISSN 0964-5691, https://doi.org/10.1016/j.

*DOI: http://dx.doi.org/10.5772/intechopen.94996*

[1] Richmond, B.M., Fletcher, C.H., Grossman, E.E., and Gibbs, A.E. (2001). Islands at Risk: Coastal Hazard Assessment and Mapping in The Hawaiian Islands. Environmental Geosciences, 8 (1): 21-37. doi: https://doi.org/10.1046/j.1526- 0984.2001.008001021.x.

[2] Francis, O., Brandes, H., Zhang, G., Ma, D., Yang, L., Doygun, O., Togia, H., Rossi, C., and Costanzo, G. (2019). State of Hawaii Statewide Coastal Highway Program Report. Prepared for the State of Hawaii Department of Transportation, Project Number HWY-06-16, August 21, 2019, https://hidot. hawaii.gov/highways/files/2019/09/ State-of-Hawaii-Statewide-Coastal-Highway-Program-Report\_Final\_2019.

[3] Hess, K., Schmalz, R., Zervas, C., and Collier, W. (1999). Tidal constituents and residual interpolation (TCARI): A new method for the tidal correction of bathymetric data. NOAA Technical Report, NOS CS 4, 99.

[4] Francis, O., Yang, L., Togia, H., and Tumino Di Costanzo, G. (2020). Ocean Hazards Database (OHD) for the State of Hawaii Statewide Coastal Highway Program Report. Mendeley Data, v11 http://dx.doi. org/10.17632/7p3hyypmjm.11.

[5] Gornitz, V. (1991). Global coastal hazards from future sea level rise. Palaeogeography Palaeoclimatology

doi:10.1016/0031-0182(91)90173-O.

[6] Brandes, H., Doygun, O., Rossi, C., Francis, O., Yang, L., and Togia, H. (2019). Coastal Road Exposure Susceptibility Index (CRESI) for the State of Hawaii Statewide Coastal Highway Program Report. Mendeley Data, v2 http://dx.doi.org/10.17632/

Palaeoecology, 89, 379-398,

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*Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future… DOI: http://dx.doi.org/10.5772/intechopen.94996*

#### **References**

*Coastal Environments*

**Acknowledgements**

**162**

**Author details**

Oceana Francis1

\*, Linqiang Yang1

1 University of Hawaii at Manoa, Honolulu, Hawaii, USA

\*Address all correspondence to: oceanaf@hawaii.edu

provided the original work is properly cited.

2 Naval Research Laboratory, Stennis Space Center, Mississippi, USA

, Harrison Togia1

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Coastal hazard and risk not only comes in the form of the physical processes on the ecosystem or built infrastructure, but also through social perceptions, as well. Perceptions of coastal hazards and risks and community support for engineered adaptation methods are important for implementation among different stakeholder

By understanding the vulnerability of a region, we may assign what adaptation method to use in vulnerable coastal regions dealing with climate change, in particular, inundation. These engineered adaptation methods include offshore barriers, coastal armoring, elevated development, floating development, floodable development, living shorelines, and managed retreat [39]. In the future, if we want

Funding provided by the State of Hawaii Department of Transportation Highways Division, under Project Number HWY-06-16. We would like to also acknowledge our affiliations to the following at the University of Hawaii at Manoa: Department of Civil and Environmental Engineering, Sea Grant College Program, and the Coastal Hydraulics Engineering Resilience (CHER) Lab. Publications fees provided by Research & Training Revolving Fund from the Civil and Environmental Engineering Department at the University of Hawaii at Manoa.

groups (experts, businesses, and community members) [38].

to continue to live on coast, we must adapt.

and Gleb Panteleev2

[1] Richmond, B.M., Fletcher, C.H., Grossman, E.E., and Gibbs, A.E. (2001). Islands at Risk: Coastal Hazard Assessment and Mapping in The Hawaiian Islands. Environmental Geosciences, 8 (1): 21-37. doi: https://doi.org/10.1046/j.1526- 0984.2001.008001021.x.

[2] Francis, O., Brandes, H., Zhang, G., Ma, D., Yang, L., Doygun, O., Togia, H., Rossi, C., and Costanzo, G. (2019). State of Hawaii Statewide Coastal Highway Program Report. Prepared for the State of Hawaii Department of Transportation, Project Number HWY-06-16, August 21, 2019, https://hidot. hawaii.gov/highways/files/2019/09/ State-of-Hawaii-Statewide-Coastal-Highway-Program-Report\_Final\_2019. pdf.

[3] Hess, K., Schmalz, R., Zervas, C., and Collier, W. (1999). Tidal constituents and residual interpolation (TCARI): A new method for the tidal correction of bathymetric data. NOAA Technical Report, NOS CS 4, 99.

[4] Francis, O., Yang, L., Togia, H., and Tumino Di Costanzo, G. (2020). Ocean Hazards Database (OHD) for the State of Hawaii Statewide Coastal Highway Program Report. Mendeley Data, v11 http://dx.doi. org/10.17632/7p3hyypmjm.11.

[5] Gornitz, V. (1991). Global coastal hazards from future sea level rise. Palaeogeography Palaeoclimatology Palaeoecology, 89, 379-398, doi:10.1016/0031-0182(91)90173-O.

[6] Brandes, H., Doygun, O., Rossi, C., Francis, O., Yang, L., and Togia, H. (2019). Coastal Road Exposure Susceptibility Index (CRESI) for the State of Hawaii Statewide Coastal Highway Program Report. Mendeley Data, v2 http://dx.doi.org/10.17632/ frr3fsx3j6.2.

[7] Onat, Y., Marchant, M., Francis, O., and Kim, K. (2018). Coastal exposure of the Hawaiian Islands using GIS-based index modeling. Ocean & Coastal Management, 163, 113-129, ISSN 0964-5691, https://doi.org/10.1016/j. ocecoaman.2018.06.003.

[8] Onat, Y., Francis, O., and Kim, K. (2018). Vulnerability assessment and adaptation to sea level rise in high-wave environments: A case study on O'ahu, Hawai'i, Ocean & Coastal Management, 157, 147-159, ISSN 0964-5691, https://doi.org/10.1016/j. ocecoaman.2018.02.021.

[9] Holgate, S.J., Matthews, A., Woodworth, P.L., Rickards, L.J., Tamisiea, M.E., Bradshaw, E., et al. (2013). New Data Systems and Products at the Permanent Service for Mean Sea Level. Journal of Coastal Research , 29(3), 493- 504. https://doi.org/10.2112/ JCOASTRES-D-12-00175.1.

[10] PSMSL. (2018). Tide Gauge Data. Retrieved from https://www.psmsl.org/ data/obtaining/.

[11] Cleveland, R.B., Cleveland, W.S., McRae, J.E., and Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, Vol. 6, pp. 3-73.

[12] Mertz, F., Pujol, M.-I., and Faugère, Y. (2018). Product user manual (Version 4.0). Copernicus Marine Environment Monitoring Service. Retrieved from http://cmems-resources. cls.fr/documents/PUM/CMEMS-SL-PUM-008-032-051.pdf.

[13] Wöppelmann, G., and Marcos, M. (2016). Vertical land motion as a key to understanding sea level change and variability. Reviews of Geophysics, 54(1), 64-92. https://doi. org/10.1002/2015RG000502

[14] Yang, L., and Francis, O. (2019). Historical and future sea level rise rates derived by combining observed and modeled data in the Hawaiian Islands. Coastal Hydraulics Engineering Resilience (CHER) Lab, Department of Civil and Environmental Engineering, University of Hawai'i at Manoa. Mendeley Data, v1 http://dx.doi. org/10.17632/3ks4dtk29v.1.

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### *Edited by Yuanzhi Zhang and X. San Liang*

In recent years, coastal environments have drawn an increasing awareness globally, regionally, or locally as in many coastal regions, the rapid industrial, urban, and agricultural development has caused dramatic land use and land cover (LULC) changes and various water pollution events in coastal environments. These environmental consequences of human activities exacerbate the effects of regional and global climate change on the hydrological cycle between the land and ocean, and these effects are frequently hazardous or destructive to coastal regions around the world. Remote sensing and geographic information systems (GIS) technologies are thoroughly adopted and applied to monitor the dynamic change of coastal environments, such as coastal LULC, water quality, and marine ecosystem.

Published in London, UK © 2021 IntechOpen © oatawa / iStock

Coastal Environments

Coastal Environments

*Edited by Yuanzhi Zhang and X. San Liang*