National and Kapodistrian University of Athens

NATIONAL AND KAPODISTRIAN UNIVERSITY OF ATHENS

SCHOOL OF SCIENCES

FACULTY OF GEOLOGY & GEOENVIRONMENT

DEPARTMENT OF GEOGRAPHY & CLIMATOLOGY

LABORATORY OF CLIMATOLOGY & ATMOSPHERIC ENVIRONMENT

THE IMPACT OF TROPICAL CYCLONES ON ANTIGUA AND BARBUDA’S

BUILDING CODES AND STANDARDS, 1988-2018

ARRY A. SIMON – 21718

Supervisors: Dr. Panagiotis T. Nastos

Dr. Maria Hatzaki

A Thesis submitted to the National and Kapodistrian University of Athens,

School of Sciences in partial fulfilment of the requirement for the Master of Science

degree of Geography and Environment in the Department of Geology and Geo-

environment in the Laboratory of Climatology and Atmospheric Environment

Athens, April 2019

ARRY A. SIMON

ABSTRACT

Past United States President John F Kennedy states: “The greater our knowledge increases, the greater our ignorance unfolds”. This is true in the context of tropical cyclones (TCs) and climate change. As we learn more and more about these two phenomena, there is still a lot that we do not know. With the climate changing, it is said that the frequencies of these tropical systems will likely decrease or remain essentially unchanged but will increase in maximum wind speeds and precipitation rates when they do occur (IPCC, 2007).

In the last decade, policy debates about climate change shifted from seeing it as a greenhouse gas emission problem towards the acceptance that some climate change impacts are inevitable and require adaptation (Klein et al., 2007; Swart and Raes 2007;

Biesbroek et al., 2009; Jordan et al., 2010; Moser 2011). The urban environment and its infrastructure will be impacted significantly by climate change (Wilby, 2007; Auld,

2008; Stevens, 2008; Neumann, 2009).

With future climate change predictions, questions remain as to when the next ‘big’ hurricane hits if the current building standards (BSs) for Antigua and Barbuda will be satisfactory to lessen the built environment damage.

iii

LIST OF ABBREVIATIONS

ABMS - Antigua and Barbuda Meteorological Services

AF – Adaptation Fund

AMM- Atlantic Meridional Mode

AMO- Atlantic Multidecadal Oscillation

AWP- Atlantic Warm Pool

BC – Building Code

BS - Building Standard

CARICOM – Caribbean Community

CDB – Caribbean Development Bank

CDMP - Caribbean Disaster Mitigation Project

CUBiC – Caribbean Uniform Building Code

DCA – Development Control Authority

DoE – Department of Environment

EIA – Environmental Impact Assessment

EF – Engineered Facilities

ENSO- El Niño–Southern Oscillation

FEMA - Federal Emergency Management Agency

GDP – Gross Domestic Product

HAZUS-MH – Hazards U.S. Multi-Hazard

IFRC – International Federation of Red Cross

IPCC – Intergovernmental Panel on Climate Change

ITCZ - Intertropical Convergence Zone

Kts – Knots

LADP – Local Area Development Plan

Mph – Miles per hour

NOAA –National Oceanic and Atmospheric Administration

OAS - Organisation of American States

OECS – Organisation of Eastern Caribbean States

ONI - Oceanic Niño Index

GCF – Green Climate Fund

PDNA – Post-Disaster Needs Assessments

PGDM - Post-Georges Disaster Mitigation

SLP- Sea-level Pressure

SS - Saffir-Simpson

SST – Sea Surface Temperature

TC – Tropical Cyclone

SCCF – Special Climate Change Fund

SIDS – Small Island Developing States

SIRF - Sustainable Island Resource Framework

UNCHS - United Nations Centre for Human Settlements

UNDP – United Nations Development Programme

UNFCCC - UN Framework Convention on Climate Change

USAID - United States Agency for International Development

TABLE OF CONTENTS

Page

ABSTRACT ...... iii

LIST OF ABBREVIATIONS ...... iv

TABLE OF CONTENTS ...... vi

Acknowledgement ...... ix

List of Tables and Figures ...... x

1. GENERAL INTRODUCTION ...... 1

1.1 Background and Problem outline...... 1

1.2 Country study context ...... 2

2. METHODOLOGY ...... 4

2.1 Method ...... 4

3. LITERATURE REVIEW ...... 6

3.1 Climatology of Tropical Cyclones in Antigua and Barbuda ...... 6

3.2 Teleconnections ...... 8

3.2.1 El Niño Southern Oscillation (ENSO) ...... 9

3.2.2 Atlantic Meridional Mode (AMM)...... 12

3.2.3 Atlantic Multidecadal Oscillation (AMO) ...... 12

3.3 Hazard, Risk, and Vulnerability ...... 13

3.3.1 Vulnerability Risk Indicators ...... 14

3.3.2 Building hazards associated with Tropical Cyclones ...... 16

3.4 Storms and Hurricanes between 1988 and 2018 ...... 17

3.4.1 Hurricane Hugo in 1989 ...... 20

3.4.2 Hurricane Luis in 1995 ...... 20

3.4.3 Hurricane Georges in 1998 ...... 21

3.4.4 Hurricane Jose in 1999...... 22

3.4.5 Hurricane Lenny in 1999 ...... 23

3.4.6 Hurricane Omar in 2008...... 23

3.4.7 Hurricane Earl in 2010 ...... 24

3.4.8 Hurricane Gonzalo in 2014 ...... 25

3.4.9 Hurricane Irma in 2017 ...... 26

3.5 History of Building Codes in the OECS ...... 27

3.5.1 Second Edition ...... 28

3.5.2 Third Edition ...... 28

3.5.3 Fourth Edition ...... 29

3.5.4 Fifth Edition ...... 29

3.5.5 Sixth Edition ...... 29

4. RESULTS AND DISCUSSIONS ...... 31

4.1 Statistics of Tropical Cyclones ...... 31

4.2 Changes to Building Standards ...... 37

4.2.1 Post-Luis ...... 38

4.2.2 Post-Georges...... 38

4.2.3 Post-Irma ...... 40

vii

4.3 DoE Funds ...... 42

4.3.1 Revolving Fund (RF) ...... 43

5. CONCLUSIONS ...... 44

6. RECOMMENDATIONS ...... 46

References ...... 48

Appendices ...... 56

Appendix 1 ...... 56

Appendix 2 ...... 57

Appendix 3 ...... 62

viii

Acknowledgement

I would like to express sincere gratitude to my Supervisors of studies. Dr. Panagiotis

T. Nastos who believed in me from the onset of my studies and Dr. Maria Hatzaki for the many discussions we have had. Special thanks to Dr. Adelle Blair who took the time to guide and advise me on the best approach to write this research.

To my mom, Gweneth Francis and other family members, whose love and prayers were greatly appreciated and gave me the strength to carry on.

Heartfelt appreciation to the Department of Environment, the Development Control

Authority and the Antigua and Barbuda Meteorology Services for all the data and information which was used to construct this paper.

To all others who supported in any way towards the completion of this research, my sincerest thanks.

List of Tables and Figures

Page

Tables

1. ENSO effects on TC activity in the Atlantic ...... 11

2. Meteorological information of TCs from 1988 -2018 ...... 18

3. ENSO effects on TC activity in the Atlantic between 1988 and 2018 ...... 32

4. Decadal Periods for TCs activity in Antigua and Barbuda ...... 36

Figures

1. Location of Antigua and Barbuda ...... 3

2. Thesis Life Cycle ...... 5

3. Classification of TCs in the Atlantic ...... 6

4. Graph showing the number of Storms per 100 years ...... 7

5. Named Storms passing within 105 nm of Antigua – 1851 to 2015 ...... 8

6. Neutral, El Niño and La Niña phases ...... 9

7. El Niño effects in the Atlantic ...... 10

8. Figure 7. ONI from 1950 – 2018 ...... 11

9. AMM distributions from 1865-2017 ...... 12

10. 25-years phase of AMO ...... 13

11. The connection between natural hazard, risk, and vulnerability ...... 14

12. Track of Hurricane Hugo ...... 20

13. Track of Hurricane Luis ...... 21

14. Track of Hurricane Georges ...... 22

15. Track of Hurricane Jose ...... 22

16. Track of Hurricane Lenny ...... 23

17. Track of Hurricane Omar ...... 24

18. Track of Hurricane Earl ...... 25

19. Track of Hurricane Gonzalo ...... 25

20. Track of Hurricane Irma ...... 26

21. Total number of storms per year in the Atlantic (1988-2018)...... 34

22. Total number of storms per year in Antigua and Barbuda (1988-2018)...... 34

23. Percentage of storms per month ...... 35

24. Percentage of intensity ...... 35

25. Percentage of type of strike ...... 36

26. Estimates of wind speed ...... 37

27. Roof completely ripped off from Hurricane Luis in Antigua ...... 38

28. Damage to the Antigua Grammar School from Hurricane Georges ...... 39

29. Homes in Antigua left without roof from Hurricane Georges ...... 39

30. Aerial footage showing the destruction in Barbuda from Hurricane Irma .... 40

31. Destruction of a house in Barbuda due to improper roof anchoring ...... 41

32. Improper slab anchorage ...... 41

33. Structural collapse due to inadequate wall reinforcement ...... 41

34. Hurricane roof bracing and strapping for wind mitigation ...... 43

1. GENERAL INTRODUCTION

1.1 Background and problem outline

Hurricane Gilbert ravaged through the Caribbean Island of Jamaica in 1988, ripping off up to 80 per cent of roofs from homes and leaving a trail of destruction in its path

(Treaster, 1988). During which time, building and planning standards did not adequately protect the country from catastrophic damage and in its aftermath, triggered most Caribbean Islands to make provisions in fortifying the built environment by the creation and implementation of proper BSs throughout the region.

Notwithstanding the aforementioned, 8 hurricanes affected the Caribbean region between June and October 2017. Irma and Maria, both Category 5 hurricanes, struck back-to-back in September. With maximum sustained winds in excess of 185mph (161 kts), Irma became the most powerful Atlantic hurricane ever on record and affected the twin-island state of Antigua and Barbuda. Destroying or damaging nearly 90 per cent of the structures on the island of Barbuda (IFRC, 2017), it became evident that more robust BSs were required for Antigua and Barbuda.

Hurricanes occur all year round but the Atlantic basin peak season is from August through October, with 78 per cent of the tropical storm days, 87 per cent of the minor

(Saffir-Simpson Scale (SS) categories 1 and 2) hurricane days, and 96 per cent of the major (SS categories 3, 4 and 5) hurricane days occurring then (Landsea, 1993).

To manage the tremendous and critical damage from TCs in the United States, the

Federal Emergency Management Agency (FEMA) conducted a HAZUS-Multi-Hazard

(HAZUS-MH) analysis to predict loss due to floods, earthquakes, and hurricanes

(Kim, 2018). A loss analysis should consider various indicators, including meteorological indicators, the built environment, geographic vulnerability, and socioeconomic vulnerability because the risk arises from a combination of the hazards, exposure, and vulnerability (Crichton, 1999).

The problem is, however, with small island developing states (SIDS), the climate’s potential for negative impact is vastly disproportionate than the local or national systems to cope. Pieke Jr. et al., 2003 concluded that Hurricane Mitch, in 1998, was a harbinger of future disasters unless actions were taken to reduce societal vulnerability which would only be effective if focused on processes of sustainable development.

This research will examine if Antigua and Barbuda has untaken significant measures for proper climate resilience and adaptation in its BSs that may help mitigate future property damage on the islands. As a result, we seek to answer:

1. How the climatological history of tropical cyclonic activity has for the past 30

years (1988-2018) impacted and changed the BSs.

2. What makes the islands vulnerable to TC-related property damage?

3. How sufficient are the implemented measures with the changing climate of the

world?

1.2 Country study context

Antigua is located at latitude 17.0747 North and longitude 61.8175 West and approximately 40 miles due north; Barbuda is located at latitude 17.6266 North and longitude 61.7713 West. This twin-island nation, located within the Leeward Islands of the northeastern Caribbean chain (Fig. 1) is cognizant to hurricanes. Data from the

Antigua and Barbuda Meteorological Services (ABMS) indicated that between the years 1851 to 2018, a total number of 115 named storms and 52 hurricanes have affected the islands. This averages to 0.7 named storms and 0.3 hurricanes per year for the peak months of August, September, and October.

Figure 1. Location of Antigua and Barbuda (Courtesy Google Maps)

Geospatial information is a critical element of building resistance and understanding what has happened. It is a key planning tool. However, in a developing country such as Antigua and Barbuda, there is a shortage of the detailed damage data and building inventory information, since it appears that data collection is not a priority, although there have been many extreme catastrophic events. Specifically, data in small island developing states (SIDS) are not always easy to find. Usually, it is found dispersed in many different Ministries or in the drawers of product consultants who have done one study or the other. Also, many agencies believe that the data, which they do happen to generate, belongs to them and thus, it is necessary to show the importance of or mandate data sharing. Therefore, proper consideration about how to approach this research was determined after inquiries were done with a few key personnel in the country.

2. METHODOLOGY

2.1 Method

The aim of this research is to establish how BSs have changed in Antigua and Barbuda from 1988 – 2018 due to the influence of TCs. To achieve this, we must first get familiarised with the climatology of Antigua and Barbuda and determine the climatic variables that contribute to the climatology of TCs. Then, we must examine the evolution of the building code (BC) from 1988 up to current, and highlight what changes, and if those changes were attributed by the influence of TCs. Finally, upon establishing the linkage between the climatology of TCs and the BSs in Antigua and

Barbuda, we can then make definitive conclusions about the vulnerability of the country and if it is well equipped to weather future TCs as predicted due to climate change.

Works of literature, raw data and other relevant materials were obtained from the

Development Control Authority (DCA), Antigua and Barbuda Meteorological

Services (ABMS), and the Department of Environment (DoE). The data was supplemented by semi-structured interviews with key personnel from the ABMS and

DCA. A semi-structured interview is a qualitative data collection strategy which comprised of a series of predetermined but open-ended questions that allows for a less constrained formal interview. A copy of the questions can be seen in Appendix 1.

Requests for works of literature were made to the DoE, regarding various projects being implemented by the Department which addresses the building of a more resilient community.

For the completion of the thesis, a research proposal was made. Next, various pieces of literature were reviewed, pertaining to the research objectives. Sources from books, journals, web sources, conferences, etc. were cited. Next, identification and justification of how data would be collected were done, followed by empirical

4 research. Discussions and analysis of findings followed, and the research work concluded with a wrap-up.

Start

1. Produce Research Proposal

2. Literature Review

3. Empirical Y 3. Design Method(s) for Data to be Collection of Empirical collected? Data

N 4. Implement Practical Research 5. Findings

6. Conclusions and Recommendations

7. Submit Thesis!

Figure 2. Thesis Life Cycle

3. LITERATURE REVIEW

3.1 Climatology of Tropical Cyclones in Antigua and Barbuda

A TC is a rotating, organised system of clouds and thunderstorms that originates over tropical or subtropical waters and has a closed low-level circulation. TCs rotate counterclockwise in the Northern Hemisphere (NOAA, 2019). The Saffir-Simpson

Scale is used to measure the intensities of these TCs (Fig.3).

Figure 3. Classification of TCs in the Atlantic (Courtesy SREC)

The Atlantic hurricane seasons officially runs from June 1 to November 30, but there have been some years when storms occurred outside of those dates. As seen in Fig. 4, the peak of the season is from mid-August to late October (NOAA, 2019).

Figure 4. Graph showing the number of Storms per 100 years (Courtesy NOAA)

Between the years 1851 and 2018, a total of 1616 named TCs have formed in the

Tropical Atlantic (AOML, 2018). One hundred and fifteen (115), as can be seen in

Appendix 2 or 7.1 per cent TCs have affected Antigua and Barbuda, passing within

105 nautical miles (nm) or 121 statute miles (sm), giving an annual return period of every 1.45 years. It is important to note that the concept of a return period is a statistical one. The annual return period of a TC is given by

퐿 푝 = (1) 푁 where p is the average annual probability of a landfall, L is the number of landfalls for a county, and N is the number of years in the study period (Elsner and Kara 1999). The return period is the inverse of the average annual probability of a landfall

1 푇 = (2) 푃

A given return period does not mean that the design storm only occurs once in the nominated time frame. As shown in Fig 5, most of the activity occurred during the

7 months of August and September respectively, which accounts for 79 per cent (Aug

34 per cent, Sep 45 per cent) of all storms to affect the islands (Destin, 2011).

Fig 5. Named storms passing within 105 nm of Antigua – 1851 to 2015 (Courtesy ABMS)

3.2 Teleconnections

Teleconnections are referred to as simultaneous correlations between temporal fluctuations in meteorological parameters at widely distant locations on Earth

(Wallace and Gutzler, 1981). Large-scale climate features such as the El Niño–Southern

Oscillation (ENSO) (Gray 1984; Goldenberg and Shapiro 1996; Wilson 1999; Klotzbach

2007), the Atlantic Meridional Mode (AMM) (Kossin and Vimont 2007; Vimont and

Kossin 2007), and the Atlantic Multidecadal Oscillation (AMO) (Goldenberg et al.,

2001; Klotzbach and Gray 2008) have all been shown to have significant impacts on

Atlantic basin tropical cyclones. More active seasons are associated with La Niña conditions, a positive phase of the AMM, and a positive phase of the AMO.

3.2.1 El Niño Southern Oscillation (ENSO)

The ENSO (Fig. 6), is the most important teleconnection that is known to affect the activity of TCs. It is associated with changes in the variability of the sea surface temperature (SST) and sea-level pressure (SLP) over the eastern, western and central

Pacific. The warm phase of ENSO is called El Niño and it is characterized by anomalously high SST and low SLP in the eastern Pacific. The cold phase of the ENSO is referred to as La Niña and is associated with anomalously low SST and high SLP in the eastern Pacific (Gary, 1984).

Fig 6. Neutral, El Niño and La Niña phases (Courtesy Columbia University)

Gary, 1984 conclude the primary reasons why ENSO is thought to impact Atlantic basin tropical cyclone activity is through alterations in vertical wind shear (Fig. 7), along with alterations in fluctuations in tropospheric and surface temperature (Tang and Neelin 2004). Because tropospheric temperature anomalies tend to lead surface temperature anomalies by several months, the potential intensity (Emanuel 1986) for tropical cyclone formation tends to be lower following an El Niño onset, as upper- level temperatures warm prior to sea surface temperatures (Wallace et al., 1998).

Consequently, the likelihood of an active season is reduced with warm ENSO conditions.

Fig 7. El Niño effects in the Atlantic (Courtesy TWC)

Research on the impacts of various large-scale climate phenomena on Caribbean tropical cyclones is much more limited. Tartaglione et al., 2003 showed that probabilities of a hurricane landfall in the Caribbean tend to be increased during the cold phase of ENSO, likely because of reductions in vertical wind shear and increases in low-level vorticity. They subdivided the Caribbean into three regions—northern, eastern, and western—and found a significant reduction of storm frequency in the

10 northern Caribbean during El Niño years, while the frequency of storms in the eastern and western Caribbean was not altered significantly.

Table 1: ENSO effects on TC activity in the Atlantic

ENSO Phase TC activity SST Vertical Wind Moisture Rainfall

Shear

El Niño Reduced Warm in the Strong Low Reduced

eastern Pacific and

cold in the North

Atlantic

La Niña Increased Cold in the Weak High Increased

eastern Pacific

and warm in

the North

Atlantic

The Oceanic Niño Index (ONI) has become the de-facto standard that NOAA uses for identifying El Niño (warm) and La Niña (cool) events in the tropical Pacific. It is the running 3-month mean SST anomaly for the Niño 3.4 region (i.e., 5oN-5oS, 120o-

170oW). Events are defined as 5 consecutive overlapping 3-month periods at or above the +0.5o anomaly for warm (El Niño) events and at or below the -0.5 anomaly for cold

(La Niña) events (Golden Gate, 2019).

Figure 8. ONI from 1950 – 2018 (Courtesy GGWeather)

3.2.2 Atlantic Meridional Mode (AMM)

AMM has been shown to impact Atlantic basin tropical cyclones through alterations in vertical shear, convergence, low-level vorticity, and sea surface temperature.

While the AMM has its maximum amplitude during the spring months, it also appears as an important driver of Atlantic basin tropical cyclone activity during the late summer and early fall (Kossin and Vimont 2007; Vimont and Kossin 2007).

AMM is also shown to affect the North Atlantic tropical cyclone development by shifting the location of the Intertropical Convergence Zone (ITCZ) (Foltz et al., 2011).

Fig 9. AMM distributions from 1865-2017 (Courtesy Eric Webb)

3.2.3 Atlantic Multidecadal Oscillation (AMO)

The AMO has been hypothesized to be driven by fluctuations in the strength of the

Atlantic thermohaline circulation, with a positive phase of the AMO associated with a stronger thermohaline circulation (Gray et al., 1997). Vimont and Kossin (2007) hypothesize that the AMO excites the AMM on decadal time scales. Wang et al., 2008 documented that the AMO is closely related to decadal fluctuations in the size of the

Atlantic warm pool (AWP). The AMO exerts a strong influence on rainfall and hurricanes in the Atlantic, and we see larger AWP during AMO (+) than during AMO

(-). Larger-than-normal areas of the AWP (defined as the area of water greater than

28.5°C in the Caribbean Sea, Gulf of Mexico, and western North Atlantic) are associated with significantly more favourable dynamic and thermodynamic conditions for tropical cyclones.

Fig. 10. 25-years phase of AMO (Left – 1971-1994 AMO cool phrase, Right - 1953-1970 and 1995-2000 AMO warm phrase) (Courtesy Goldenerg et al., 2001)

3.3 Hazard, Risk, and Vulnerability

The UN/ISDR (2009) defines these variables: Risk is “The combination of the probability of an event and its negative consequences”; Hazard is “A dangerous phenomenon, substance, human activity or condition that may cause loss of life, injury or other health impacts, property damage, loss of livelihoods and services, social and economic disruption, or environmental damage”; and, Vulnerability refers to “The characteristics and circumstances of a community, system or assets that make it susceptible to the damaging effects of a hazard.” The disaster risk (Fig. 11) is the product of the combining elements – vulnerability and natural hazard (UN/ISDR,

2009).

Coastal environments throughout the Eastern Caribbean are increasingly at risk from sand mining activities as well as poorly planned and executed development activities that exacerbate the erosion of these landforms and increase the vulnerability of coastal

13 infrastructure to flooding (Lewsey 2004:399). While modern forecasting of hurricanes has become more reliable (Moin 2001:179), errors still exist thus changing the risk levels from one village to the next, increasing or decreasing the risk factor depending on the new forecast tracks.

When discussing the vulnerability to TCs, the risk cannot only be dependent on the forecast track for the TC but also to the strength and the duration of exposure to other forces that has an impact on the natural and built environment. Assessing the vulnerability of each community and then correlating with the specifics of the hazard to determine the risk, is a key component. Some communities due to their location, built environment, wealth or other various factors, may be more resilient than their adjacent communities.

Figure 11 – The connection between natural hazard, risk, and vulnerability (UN/ISDR, 2009)

3.3.1 Vulnerability Risk Indicators

Meteorological Indicators

Various numbers of meteorological indicators could play a crucial role in determining damage. The strength of a hurricane is determined by the combination of the forward motion speed, maximum wind speed, and radius of maximum wind speed (Burton,

2010) with the maximum wind speed and the radius of maximum wind speed being the main indicators (Dunion, 2003 and Watson, 2004).

The volume of precipitation also strongly influences the damage by a storm. The strength of the wind forces in a storm has little to no bearing on the amount of rain which can be delivered. Further, it has occurred that TDs with large precipitation loads have done more damage than some hurricanes. Brody et al., 2008 investigated the relationship between flood damage and the built environment (Wasson, 2004).

They found precipitation to be the most powerful indicator of loss among the variables they examined. Choi and Fisher also found a positive relationship between catastrophe loss and precipitation (Choi and Fisher, 2003).

Geographic Vulnerability Indicators

The geographic vulnerability is a vital determinant of damage from natural hazards because vulnerability represents the exposure to risk (Cutter, 1996). The diverse geographic characteristics vary greatly by site, and they influence the amount of exposure to natural disasters. These characteristics include altitude, distance from water, distance from water management infrastructure, and other environmental indicators (Highfield, 2010). During TCs, coastal areas are more vulnerable since they usually experience greater high gusts and storm surges than inland areas.

Built Environment Vulnerability Indicators

The built environment vulnerability arises from the man-made infrastructures and the structural vulnerability of the building (Khanduri, 2003 and Kim, 2015). The structural vulnerability of a building is fundamentally correlated with the built environment vulnerability to a natural disaster. A construction such as a slope excavation and deforestation for road cuts can increase the occurrence of landslides (Cui, 2009 and

Zhai, 2007). Construction along a slope reduces the stability of the site and increases slope failures (Dai, 2002). Ayalew and Yamagishi statistically examined the relationships of landslides with several risk indicators and found that slope works are connected with the occurrence of slope failure (Ayalew and Yamagishi, 2005). The structural vulnerability also plays a dominant role in the amount of damage from natural disasters (Chock, 2005 and SSRN, 2014). Moreover, the number of stories in a building affects its vulnerability to windstorm damage. A study conducted by

Khanduri found that a building’s height is associated with the amount of damages

(Khanduri, 2003).

3.3.2 Building hazards associated with Tropical Cyclones

TCs are associated with various distinct hazards that can damage buildings, industrial facilities and infrastructure (hereafter, these are referred to as engineered facilities).

Specifically, engineered facilities (EF) serve as society's first barrier against TCs.

The major sources of the damages to EF due to TCs are strong wind associated with

TCs, heavy rain including flood and landslide, and storm surge and wave.

1. Strong wind caused by TCs is one of the major sources of damage to EF in TC-

prone regions. Damage can be categorized into two broad types:

- damage to structural components

- damage to claddings and facade elements

The first type of damage may lead to structural failures of EF and is a major and

relevant concern in less developed countries and for offshore facilities.

The consequences due to the second type of damages are often significant in

that once the building envelope is breached significant quantities of rainwater

can enter the building causing damage to the building interior and contents.

2. Heavy rain often accompanies TCs. Rain may damage engineered facilities,

especially onshore, through a variety of mechanisms. Firstly, rain can directly

damage a structure's contents when rainwater penetrates a structure's exterior

envelope. It is important to note here however that this damage is of great

relevance to the insurance industries. In Antigua and Barbuda, insurance

companies do not include damages from flooding as rain exposure damage,

even if caused by excessive rainfall. Property owners are therefore required to

have flood insurance separate, in addition to storm insurance. Secondly,

freshwater floods can arise as a consequence of heavy rain. Thirdly, heavy rains

can induce landslides (both shallow and deep-seated). The second and third

damage mechanisms are relevant especially for societal decision makers for

land planning and disaster mitigation.

3. Storm surge is caused by a combination of high wind pushing water towards

the shore and low atmospheric pressure allowing the ocean surface to rise.

Large waves produced by high winds are often not accounted for in storm

surge estimates. The action of breaking waves near the shore results in

additional effective stresses that act to increase the height of the storm surge

and is termed wave setup.

3.4 Storms and Hurricanes between 1988 and 2018

Between 1988 and 2018 (Table 2), 21 of those years have had direct or indirect impacts from 38 storms. Notable hurricanes such as Hurricane’s Hugo (1989), Luis (1995),

Georges (1998), Jose (1999), Lenny (1999), Omar (2008), Earl (2010), Gonzalo (2014) and

Irma (2017), triggered the BSs in Antigua and Barbuda to be changed throughout the years to mitigate th