Natural Hazards (2006) DOI 10.1007/s11069-006-0007-9
Springer 2006
Validating a Tsunami Vulnerability Assessment Model (the PTVA Model) Using Field Data from the 2004 Indian Ocean Tsunami DALE DOMINEY-HOWES1,w and MARIA PAPATHOMA2 1
Department of Physical Geography, Risk Frontiers, Macquarie University, Sydney, New South Wales 2109, Australia; 2Department of Photogrammetry and Remote Sensing, University of Technology, Gusshausstrasse 27-29 1040, Vienna, Austria (Received: 18 June 2005; accepted: 3 February 2006) Abstract. The ‘‘PTVAM’’ tsunami vulnerability assessment model [Papathoma and DomineyHowes: 2003, Nat. Hazards Earth Syst. Sci. 3, 733–744; Papathoma et al.: 2003, Nat. Hazards Earth Syst. Sci. 3, 377–389], like all models, requires validation. We use the results from posttsunami surveys in the Maldives following the December 26, 2004 Indian Ocean tsunami to ‘evaluate’ the appropriateness of the PTVAM attributes to understanding spatial and temporal vulnerability to tsunami damage and loss. We find that some of the PTVAM attributes are significantly important and others moderately important to understanding and assessing vulnerability. Some attributes require further investigation. Based upon the ground-truth data, we make several modifications to the model framework and propose a revised version of the PTVAM (PTVAM 2). Key words: tsunami, vulnerability assessment model, validation, Maldives
1. Introduction and Aims Studies of (natural) hazard, risk and vulnerability have increased significantly in the last 20 years leading to a marked improvement in our understanding of core issues at the heart of these concepts (Alexander, 2000; Wisner et al., 2004). In many cases, models have been developed to aid in the process of understanding, assessing and mapping hazard, risk and vulnerability and these models may be conceptual, practical, or analytical (Contini et al., 2000; Jenkins, 2000; Fischer et al., 2002; Gambolati et al., 2002; Zerger et al., 2002; Cheung et al., 2003). It is widely understood that models need validation to ensure that they accurately reflect processes operating in the ‘real-world’ – thus making them useful for prediction. Validation of hazard, risk and vulnerability models within the realm of natural w
Author for correspondence: E-mail:
[email protected]
DALE DOMINEY-HOWES AND MARIA PAPATHOMA
disasters is especially important since model data outputs are often used to determine land use zoning and planning, emergency response actions, disaster planning and insurance premiums (Tufekci, 1995; Esogbue, 1996; Pidd et al., 1996; Chang et al., 1997; Peters and Hall, 1999; Jenkins, 2000). In recent years, efforts have been made using Geographic Information System (GIS) frameworks to develop vulnerability assessment models for many types of natural hazard. One such model, the ‘‘Papathoma Tsunami Vulnerability Assessment Model’’ (or PTVAM [model] for short) (Papathoma, 2003; Papathoma and Dominey-Howes, 2003; Papathoma et al., 2003) was developed to investigate the vulnerability of coastal areas to tsunami. The PTVAM, like all models requires careful validation on the basis of empirical field data collected following a major tsunami. The 2004 Indian Ocean tsunami although catastrophic, offers an important opportunity to ‘ground-truth’ the various components or attributes of the PTVAM. In light of the introduction, the aims of this paper are (1) to briefly restate the core components (attributes) of the PTVAM; (2) to present selected results from several post-tsunami assessment surveys from specific islands within the South Male’ (Kaafu) Atoll, the Republic of the Maldives as they relate to the core components of the PTVAM; (3) to evaluate the relevance of various PTVAM components in light of the impacts of the 2004 Indian Ocean tsunami on the ground; and (4) to make recommendations for the improvement of the PTVAM framework.
2. Core Elements of the PTVAM The PTVAM is a dynamic model that incorporates multiple parameters (hereafter referred to as ‘attributes’) that are known to influence vulnerability to tsunami loss and damage. The model is dynamic in that the attribute data contained within the primary database, may be modified and updated allowing investigation of vulnerability both spatially and temporally. The PTVAM is organised and presented within a GIS framework thus allowing rapid data entry and visualisation of changing vulnerability (through the production of new maps). Based on an analysis of multiple post-tsunami field surveys, Papathoma (2003) identified a suite of ‘attributes’ (parameters) that are reported to affect the degree of damage from, or protection to, tsunami flooding for individual buildings and structures. These attributes were then classified in to ‘major classes or groups’ (e.g. built environment, sociological data, economic data and environmental/physical data) and variations (values) in the degree or range of these attributes were identified together with a vulnerability descriptor for each attribute. These vulnerability classes/groups, parameters, values and descriptors are given in Table I.
Sociological data
Number of stories in each building Description of ground floor
The Built Environment
Number of people per building
Population density
Movable objects
Building material, age, design
Building surroundings
Parameter (attributes)
Major class
Population Population Population Population Many Few
density density density density
during during during during
the the the the
night day summer winter
Only one floor More than one floor Open plan with movable objects, e.g. tables and chairs Open plan or with big glass windows without movable objects None of the above No barrier Low/narrow earth embankment Low/narrow concrete wall High concrete wall Buildings of fieldstone, unreinforced, crumbling and/or deserted Ordinary masonry brick buildings, cement mortar, no reinforcement Precast concrete skeleton, reinforced concrete Movable objects present No movable objects present
Value
Vulnerability may be Vulnerability may be Vulnerability may be Vulnerability may be High vulnerability Low vulnerability
Low vulnerability High vulnerability Low vulnerability high high high high
Moderate vulnerability
Low vulnerability Very high vulnerability High vulnerability Moderate vulnerability Low vulnerability High vulnerability
Moderate vulnerability
High vulnerability Low vulnerability High vulnerability
Vulnerability descriptor
Table I. Vulnerability classes/groups, attributes, values and descriptor as outlined in the PTVAM framework.
or or or or
low low low low
VALIDATING A TSUNAMI VULNERABILITY ASSESSMENT MODEL
Land use
Physical or man-made barriers/sea defence
Economic data
Environmental/physical data
Land cover (vegetation)
Natural environment
Parameter (attributes)
Major class
Table I. Continued.
Natural (sandy beach or marsh offering low protection) Soil embankment (offers moderate protection against flooding) Concrete stone wall (offers high protection against flooding) Wide intertidal zone Intermediate intertidal zone Narrow intertidal zone No vegetation cover Scrub and low vegetation Trees and dense scrub
Business (shops, taverns, hotels etc.) Residential Services (schools, hospitals, power stations etc.)
Value
Low vulnerability Moderate vulnerability High vulnerability High vulnerability Moderate vulnerability Low vulnerability
Low vulnerability
Moderate vulnerability
High vulnerability
No value required No value required No value required
Vulnerability descriptor
DALE DOMINEY-HOWES AND MARIA PAPATHOMA
VALIDATING A TSUNAMI VULNERABILITY ASSESSMENT MODEL
The PTVAM approach was designed to be sensitive and capable of examining vulnerability at high resolution scales (i.e. a ‘detail bottom-up model’ at building to building scale) rather than at a coarse scale where entire street rows are catagorised as the same (i.e. an aggregate model). Papathoma (2003), Papathoma and Dominey-Howes (2003) and Papathoma et al. (2003) applied the PTVAM to selected urban coastal areas in Greece. Figure 1 demonstrates the type of information and results that may be obtained using this method.
3. Islands Visited in this Study One of us (DD-H) was able to participate in multiple post-tsunami field surveys in the Republic of the Maldives following the Indian Ocean tsunami of December 26, 2004. These surveys examined the effects of the tsunami on individual communities, their infrastructures and livelihoods and the natural environment and were undertaken in collaboration with experts from the Government of the Republic of the Maldives (specifically from the Ministry of Finance, the Ministry of Planning and the Marine Research Centre), the Asian Development Bank, the United States and Australia. The data described here come from investigation of islands within South Male’ Atoll (Figure 2). South Male’ Atoll forms part of the atoll administration area called ‘Kaafu’. Islands that were visited and surveyed are (in order of study) Biyadhoo, Vilivaru, Guraidhoo, Lhosfushi, Kandooma, Maafushi and Cocoa (Figure 2). These islands were chosen because (1) it was possible to remain in contact with the National Disaster Management Centre on a daily basis; (2) logistically it was convenient to be based at Male’ and travel only short distances; (3) it was known that these islands (all within a short distance of each other) had experienced various levels of inundation and impact from minor to major; (4) no other international field survey teams had visited these islands and our team was able to fill a spatial gap in coverage left by other field teams; and (5) permission was granted to work at Kandooma Island Resort.
4. Identification of Tsunami Impacts in the Maldives as Related to the PTVAM Here, selected data from the post-tsunami surveys on Guriadhoo and Kandooma islands are presented since they (the damage data) closely relate to the vulnerability attributes identified in the PTVAM framework.
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Figure 1. (Upper) Map showing buildings, venues (e.g. sports stadiums) and open spaces with high population density (shown in pink) (and thus high human vulnerability) during the summer in the western part of Heraklieo, Crete. (Lower) the same but for the winter. It is clear that population density and vulnerability are much higher during the summer as more people are on the beaches and using specific venues. Such temporal variation in vulnerability ought to be considered when developing emergency response and disaster plans.
VALIDATING A TSUNAMI VULNERABILITY ASSESSMENT MODEL
150
0
km
.
The Maldives
Male’ Indian Ocean
South Male’ Atoll
300
North Male’ Atoll
Laccadive Sea
MALE’
Hulule
ligili
Vi
1000 500
South Male’ Atoll
Maafushi Biyadhoo Vilivaru
Cocoa
Kandooma Guraidhoo Lhosfushi
2000 10
0
km
Figure 2. South Male’ Atoll (Kaafu) and the islands on which post-tsunami surveys were conducted.
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4.1.
THE BUILT ENVIRONMENT
Number of stories in each building – in almost every case, Maldivian buildings are constructed with just one floor (Figure 3). The exception is with the capital Male’ where a shortage of space has resulted in a steady increase in the construction of taller buildings. As a consequence, survivors and staff at the National Disaster Management Centre stated that as the tsunami flood water rose, people had to climb on to the roofs of their houses and other buildings. Due to the single story nature of construction however, no further vertical evacuation is possible. This is considered a major problem since in many instances, tsunami flood waters reached the roof tops. Open plan with movable objects or with big glass windows – a particular problem observed at tourist resorts was the open plan nature of the main resort buildings and communal areas (Figure 4). For example, the design form of these structures is for open plan, spacious communal areas (dining halls, entertainment areas etc.). These open plan areas are filled with chairs, tables and other facilities for guests. Survivors and resort managers (together with television news pictures shown around the world in the days after the tsunami) reported or showed the entrainment and transport of small non-fixed objects within the tsunami flood water. These objects are
Figure 3. A typical one floor home in the Maldives. Note that these structures are built upon a concrete slab floor and have little in the way of structural reinforcing. The major walls are actually made of coral rock blocks cemented using lime mortar. The lack of upper floors meant that the occupants had no opportunity to retreat upstairs, resulting in enhanced vulnerability to injury or death during the tsunami.
VALIDATING A TSUNAMI VULNERABILITY ASSESSMENT MODEL
Figure 4. Open plan layout of the main communal areas of the Coca Resort Island hotel complex. Note how low lying and close to sea level the complex is. This island and its infrastructure were hardly affected although Kandooma Resort Island (approximately 300 m distant) were severely affected. Such open plan areas (and the people occupying them) are highly vulnerable to damage, injury and death associated with impacts from objects entrained within tsunami flow through these spaces.
believed to have caused enormous loss of life, injury and damage to fixed structures. Vivid footage of such tsunami flow through open plan building areas is available at: http://www.asiantsunamivideos.com. Building surroundings (no barrier, low/narrow earth embankment, low/ narrow concrete wall, high concrete wall) – many houses, infrastructural units (e.g. electricity generating stations), public buildings (e.g. Island Chiefs Office) and alike had small to moderately sized reinforced concrete walls around their perimeters (Figure 5). In certain instances, these walls acted as a barrier preventing, or at least reducing, both the volume and velocity of water that entered the structure behind the wall. Building material, age, design – within the Maldivian islands, buildings – particularly residential buildings may be generally classed in to three types: (type 1) unreinforced field stone (coral blocks); (type 2) concrete slab floor, concrete columns and cross-beams and unreinforced field stone (coral blocks) fill in the walls and; (type 3) reinforced concrete structures (slab floors, walls, vertical and horizontal columns) (Figure 6). Type 1 is generally the indigenous form and often the oldest homes and buildings
DALE DOMINEY-HOWES AND MARIA PAPATHOMA
Figure 5. Reinforced concrete wall surrounding the hospital for people with special needs on Guraidhoo. The presence of the retaining wall helped to prevent tsunami flooding of the compound behind. Survivors reported that because the hospital compound was protected from flooding, many people took refugee here between the first and second tsunami waves.
are constructed this way. Type 3 structures are the newest. These three building types were variously affected by the tsunami. Those of type 3 were often flooded but the structures themselves sustained little if any damage. Damage reported included broken glass windows and broken doors and window frames. Occasionally, moderate to severe erosion of surface sediments around the corners of these buildings occurred (Figure 6). Type 2 buildings sustained various degrees of damage from minor (such as broken windows and doors) to major (such as collapse of walls and roofs). Type 1 (unreinforced fieldstone) buildings suffered the most extensive and severe forms of damage (and indeed, complete destruction). Depending on the force and depth of tsunami flooding, type 1 buildings frequently experienced total collapse, major collapse of one or more exterior walls and loss of all windows and doors. Presence of movable objects – it has already been noted that the presence of large numbers of small non-fixed removable objects within the flood zone was a major problem. Such movable objects entrained within tsunami flood waters caused four problems: (1) death and injury of people; (2) damage/destruction to buildings through hydrodynamic
VALIDATING A TSUNAMI VULNERABILITY ASSESSMENT MODEL
Figure 6. (Upper) Photo of severely damaged poorly constructed houses on Maafushi island. Note the collapse of the unreinforced walls constructed of coral block field stone in front of the houses. (Lower) Well constructed building on Maafushi that has sustained only slight superficial damage. There has been some erosion of ground sediments around the edge of the base of this building.
DALE DOMINEY-HOWES AND MARIA PAPATHOMA
impact; (3) environmental pollution when pollutants were transported across the landscape and in to lagoons; and (4) blockage and obstruction of evacuation routes (where present). 4.2.
SOCIOLOGICAL DATA
Population density during the day–night/different times of the year – in comparison with other coastal areas affected by the tsunami (e.g. Indonesia and Sri Lanka), total population numbers and density within the Maldivian islands is relatively low. However, the total population on individual islands (e.g. the tourist islands) can vary significantly from season to season. Some ‘tourist’ islands (such as Kandooma) were severely affected by the tsunami. At the time of the tsunami, Kandooma Resort Island had 210 guests and 55 staff members in residence and all guests (and many staff) had to be evacuated. If the tsunami had occurred outside the tourist season, then total population of this and other islands, would have been much less. The Indian Ocean tsunami came at the worst time of the year for the Maldives – that is, when tourist arrivals are at their highest. Number of people per building – the absolute number of people per building in the islands visited is not known. 4.3.
ECONOMIC DATA
Land use – individual buildings within the tsunami inundated areas were either private homes, public facilities (e.g. Island Chiefs office, electricity generating station, medical facility, school, mosque etc.), private businesses (e.g. food store, tourist souvenir shop etc.) or empty. Depending on the type of use of an individual building, two vulnerability related factors are apparent. Firstly, if the building is ‘public’ in terms of it being constructed by the government, it is constructed to a much higher standard than private buildings. As such, these public buildings generally sustained less damage. Secondly, privately owned buildings (homes or businesses) tended to be of a poorer construction standard and generally sustained greater damage. Additionally, the owners of these privately constructed dwellings and businesses rarely (if at all) have insurance. Therefore, the effect of the tsunami is magnified in that their loss is not automatically compensated. This is particularly so for private businesses although the national government is helping to reconstruct lost and damaged homes (Waheed, 2005, personal communication). 4.4.
ENVIRONMENTAL/PHYSICAL DATA
Physical or man-made barriers/sea defences – the Maldives is very vulnerable to catastrophic marine inundation because of its low elevation. Average
VALIDATING A TSUNAMI VULNERABILITY ASSESSMENT MODEL
height of the land above sea level is just +1.5 m. Consequently, Male’ the capital, is completely surrounded by reinforced concrete walls that act as a flood barrier. Tetrapods have been placed along sections of this seawall to reduce wave energy. The sea wall appears to have helped to reduce the maximum tsunami flood height in the streets of Male’ (although bathymetry may have had some control over the low flood heights recorded in Male’). For other Maldivian islands, the absence of any protective structures at the coast resulted in uninterrupted inundation of the islands by individual tsunami waves. Width of the intertidal zone – for islands located at the seaward edge of the atolls, there is essentially no intertidal zone. Narrow fringing reefs surround some islands and off-shore water depth drops rapidly to hundreds or thousands of metres. Therefore, little if any protection seems to have been afforded by the presence of narrow fringing reefs. For example, the eastern coast of Guraidhoo island is bounded by a fringing reef that is no more than 400 m wide. Maximum measured tsunami flood height on Guraidhoo was +3.37 m above sea level. The narrow intertidal zone, lack of a protective reef and poor construction of buildings resulted in major structural failure of buildings adjacent to the eastern shoreline (Figure 7).
House totally destroyed
Direction of tsunami wave approach
House damaged but repairable House damaged beyond repair - needs complete replacement
Plantations and gardens
Cemetry
F staootba diu ll m
Uthuru avashu school Home for people with special needs Power Station
Guraidhoo school Island Office / court
Mosque
Jetty
N
Health centre
Scale 1:600
Figure 7. Map of Guraidhoo Island, South Male’ Atoll. Note the total destruction and wider damage pattern of buildings to tsunami on the island. Of interest is the damage to those buildings on the western (lee ward) side of the island. Damage to these buildings was associated with ‘wrap-around’ of the tsunami.
DALE DOMINEY-HOWES AND MARIA PAPATHOMA
Land cover/vegetation – the presence or absence of dense coastal vegetation at the edges of the islands appears to have been significant in influencing the degree of damage to structures landward of the coastal vegetation (Pearson, 2005, Williams, 2005). For example, at Kandooma, the type and severity of damage appeared directly related to the presence/absence of vegetation between the structures and the sea (Figure 8). The resort comprises 102 rooms. 24% of rooms suffered major structural damage. Fortyfour percentage of rooms suffered moderate structural damage. Thirty-two percentage of rooms suffered minor structural damage. Eighty-two percentage of rooms suffered total contents loss (soft furnishings, furniture etc.). Of the resort units that suffered ‘major’ structural damage, no vegetation that might act as a flood barrier was present between the shore and the unit in every case. Of the resort units that suffered ‘moderate’ structural damage, no vegetation that might act as a flood barrier was present between the shore and the unit in 51% of cases. 5. Evaluation of the PTVAM Attributes Having outlined the core attributes of the PTVAM and presented posttsunami survey results that relate to these attributes, we now evaluate their relevance to understanding vulnerability. We use a semi-quantitative Likert scale evaluation that is based upon author observation, discussion with engineers, Maldive government officials and other field scientists. For each PTVAM attribute, we reflect on the field data and ask, to what degree is this attribute relevant to loss or damage caused by the tsunami? Answers may be classified as: • • • • •
not at all important slightly important moderately important significantly important needs further assessment to determine.
5.1.
THE BUILT ENVIRONMENT
Number of stories in each building – the number of floors is critically important for determining the vulnerability of people occupying a structure. Where more than one story is present, occupants are able to evacuate vertically. Where vertical evacuation is not possible, occupants are at significantly increased risk of suffering injury or death. Buildings with multiple levels are highly desirable and may have resulted in a smaller number of injuries and deaths following the Indian Ocean tsunami (Waheed, 2005, personal communication). The PTVAM is right to identify
VALIDATING A TSUNAMI VULNERABILITY ASSESSMENT MODEL
Maafushi Biyadhoo Cocoa
Vilivaru
Kandooma Guraidhoo Lhosfushi Direction of tsunami wave approach
101 Small harbour
0
350 m
Sw po imm ol ing
N
Reception
Arrivals jetty Dining Hall Voll
406
ey B
Groynes
cour
t
all
302 305
206 211
309 311
Figure 8. Sketch map of Kandooma Resort Island, South Male’ Atoll. The 102 rooms (units) on the island are arranged in a ring around the outer perimeter. Rooms that sustained the least amount of damage were protected by the presence of dense vegetation between the shoreline and the unit (rooms in the 200s and the 400s). Room units in the 100s and 300s sustained the greatest amounts of damage (see Figure 9).
this attribute as relevant to assessing vulnerability. It is therefore, ‘‘significantly important’’. Open plan with movable objects or with big glass windows – we observe mixed results in relation to this attribute. Open plan means tsunami may ‘punch’ straight through the ground floor thus limiting structural damage
DALE DOMINEY-HOWES AND MARIA PAPATHOMA
Figure 9. Major structural failure of exterior wall in room 104, Kandooma Island. The sea is to the right of the photo and almost no vegetation is present between the room and the shore.
to buildings. However, presence of other objects floating within the tsunami flood waters may still cause structural damage. Buildings that present solid walls at right angles to the direction of flow appear to be at increased risk from damage or failure. The actual degree of damage seems dependent on the type, age, style of construction, wave height, force of surge (including flow velocity) and the presence of debris that may cause additional hydrodynamic impacts. The PTVAM is right to identify this attribute as relevant to assessing vulnerability. However, this attribute ‘‘needs further assessment to determine’’. Building surroundings (no barrier, low/narrow earth embankment, low/ narrow concrete wall, high concrete wall) – the presence of a surrounding barrier does seem to be of some importance in influencing vulnerability to damage and loss. The actual degree of protection offered by a barrier is dependent upon its height, material, age, how well it is constructed, distance from shore, depth and velocity of flood water, the effects of floating debris and so on. The PTVAM is right to identify this attribute as relevant to assessing vulnerability. It is therefore, ‘‘significantly important but needs further assessment to determine’’. Building material, age, design – these attributes are highly significant in determining the degree and severity of damage and destruction to
VALIDATING A TSUNAMI VULNERABILITY ASSESSMENT MODEL
structures. This was clearly evident in all islands visited. The PTVAM is right to identify this attribute as relevant to assessing vulnerability. It is therefore, ‘‘significantly important’’. Presence of movable objects – the presence of movable objects appears critical for four reasons. Firstly, water entrained debris may cause death and injury to people. Secondly, the debris may cause damage to or destruction of buildings and structures. Thirdly, debris transported by the tsunami may cause pollution of the natural environment and ecosystems. Fourthly, debris may block designated evacuation and rescue routes. All these effects were evident in the islands surveyed. The PTVAM is right to identify this attribute as relevant to assessing vulnerability. It is therefore, ‘‘significantly important’’. 5.2.
SOCIOLOGICAL DATA
Population density during the day–night/different times of the year – this attribute is relevant to determining vulnerability. Population density requires careful consideration and analysis. For example, beaches, hotels, schools, hospitals, sporting stadiums and so forth are all occupied at different times of the day and year. Therefore, it will be necessary for appropriate authorities to be aware of the various periods of high density occupation. The PTVAM is right to identify this attribute as relevant to assessing vulnerability. It is therefore, ‘‘significantly important but needs further assessment to determine’’. Number of people per building – we were not able to determine any meaningful data on this attribute from our field observations. However, we believe this attribute is very important because knowledge of where people actually are, can ‘‘guide’’ effective rescue operations. Therefore, this attribute ‘‘needs further assessment to determine’’. 5.3.
ECONOMIC DATA
Land use – in the Maldives, the use of an individual building or structure did seem to be important in determining both its vulnerability to damage or destruction and whether its occupants would likely receive immediate and full compensation from the government. This ‘vulnerability’ is in part physical (the building materials), and in part, socio-political (structural). As such, the context of economic vulnerability is likely to vary significantly from one place to another. The PTVAM is right to identify this attribute as relevant to assessing vulnerability although the degree to which it is relevant will vary significantly from one place to another. As such, location-specific assessments that take account of socio-political constraints will always be required. Therefore, this attribute is ‘‘significantly important but needs further assessment’’.
DALE DOMINEY-HOWES AND MARIA PAPATHOMA
5.4.
ENVIRONMENTAL/PHYSICAL DATA
Physical or man-made barriers/sea defences – the presence (or in the case of the Maldives, the absence) of wide fringing reefs does appear to be significant in influencing the degree of damage and loss at the coast. The PTVAM is right to identify this attribute as relevant to assessing vulnerability. It is therefore, ‘‘significantly important’’. Width of the intertidal zone – we were not able to determine in this study the degree of influence of intertidal zones (wide intertidal zones with wide beaches) due to the absence of such features. However, we still believe (based upon the effects of the fringing reefs) that this attribute is likely to be important to determining vulnerability. Therefore, this attribute ‘‘needs further assessment to determine’’. Land cover/vegetation – does appear to be of critical importance in reducing damage and loss at coastal sites. The PTVAM is right to identify this attribute as relevant to assessing vulnerability. Therefore, this attribute is ‘‘significantly important but needs further assessment’’ (in terms of species composition and density). 6. Recommendations From the information presented in the preceding sections, it is clear that many of the attributes identified in the PTVAM framework are relevant to understanding vulnerability to damage and loss. Furthermore, others may be relevant but were unable to be tested in this investigation. Based upon our analysis, we recommend revisions and improvements to the PTVAM framework. We outlined the core components of the first version of the PTVAM framework in Table I. Here we present a new version of the PTVAM framework (hereafter referred to as PTVAM 2) taking account of lessons learnt from post-tsunami assessment surveys in the Maldives. These revisions to the PTVAM framework may be grouped as: (1) changes in the names and types of ‘‘major classes’’; (2) changes in the types of ‘‘attributes’’ of each major class; (3) changes in the attribute descriptor’’; and, (4) changes in the ‘‘vulnerability descriptor’’. The components of the revised PTVAM (PTVAM 2) are presented in Table II.
7. Discussion and Conclusions The PTVAM framework was developed and originally tested in the Greek area of the Mediterranean Sea. The Indian Ocean tsunami of 26th December 2004 has, sadly, provided an important opportunity to examine
Number of stories in each building Description of ground floor
The Built Environment
Shape and orientation of building
Building material, age, design
Building surroundings
Attribute
Major class
Table II. The revised PTVAM 2 framework.
Only one floor More than one floor Open plan with movable objects, e.g. tables and chairs Open plan or with big glass windows without movable objects None of the above No barrier Low/narrow earth embankment Low/narrow concrete wall High concrete wall Buildings of fieldstone, unreinforced, crumbling and/or deserted Ordinary masonry brick buildings, cement mortar, no reinforcement Precast concrete skeleton, reinforced concrete Square or oblong shaped structure Non-cubic shaped building (e.g. has hexagonal or circular shaped floor plan)
Attribute description
High vulnerability Low vulnerability
Low vulnerability
Moderate vulnerability
Low vulnerability Very high vulnerability High vulnerability Moderate vulnerability Low vulnerability High vulnerability
Moderate vulnerability
High vulnerability Low vulnerability High vulnerability
Vulnerability descriptor
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Infrastructural frameworks/ awareness
Population data
Major class
Table II. Continued.
Government provides full relief/compensation Government provides some relief/compensation Government provides no relief/compensation
Government relief structures
Number of people per building
Population density during the night Population density during the day Population density during the summer Population density during the winter Many Few
High vulnerability
Walls parallel to shore line Corners of building facing the shore line Residential Services (schools, hospitals, power stations etc.) Business (shops, taverns, hotels etc.)
Low vulnerability Moderate vulnerability High vulnerability
Vulnerability may be high or low Vulnerability may be high or low Vulnerability may be high or low Vulnerability may be high or low High vulnerability Low vulnerability
No value required
No value required No value required
Low vulnerability
Vulnerability descriptor
Attribute description
Population density
Building use
Attribute
DALE DOMINEY-HOWES AND MARIA PAPATHOMA
Natural environment
Environmental data
Land cover (vegetation)
Physical or man-made barriers/sea defence
Defensive structures
Household/business awareness and pre-planning
Insurance policy
Wide intertidal zone Intermediate intertidal zone Narrow intertidal zone No vegetation cover Scrub and low vegetation Trees and dense scrub
No protective barriers Soil embankment (offers moderate protection against flooding) Concrete stone wall (offers high protection against flooding)
Household/business has tsunami insurance cover Household/business does not have tsunami insurance cover Resident/owner aware of tsunami risk and has acted accordingly Resident/owner aware of tsunami risk but has not acted Resident/owner not aware of tsunami risk and not able to act
Low vulnerability Moderate vulnerability High vulnerability High vulnerability Moderate vulnerability Low vulnerability
Low vulnerability
High vulnerability Moderate vulnerability
High vulnerability
High vulnerability
Low vulnerability
Low vulnerability High vulnerability
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DALE DOMINEY-HOWES AND MARIA PAPATHOMA
to what degree the founding assumptions of the model framework, together with its attributes, effectively identify vulnerability to damage and loss during a tsunami flood. It is clear that the attributes identified within the PTVAM 2 framework are appropriate and robust. Validation of the PTVAM 2 framework means that it may confidently be transferred and applied to coastal landscapes in geographically different (and distant) settings and for tsunami of varying magnitudes. We do recognise however, that other factors (e.g. variations in offshore bathymetry, shape of the coastline, onshore relief, angle of wave approach, number of tsunami waves etc.) have an influence on the magnitude of the tsunami at specific locations. As such, these other parameters are also likely to influence the degree of damage sustained but are beyond the nature of our model. Furthermore, it appears that the PTVAM 2 framework may be confidently applied to coastal landscapes in both developed and developing countries. This should be considered a valuable finding since it ought to permit those tasked with the responsibility of emergency and disaster planning, to focus limited disaster response resources highly effectively. The 2004 Indian Ocean tsunami whilst not the largest (from a geophysical perspective), certainly was the most catastrophic on record in view of the human losses, suffering and effects on coastal communities and infrastructure. Understanding those attributes of the natural and human systems that make people, structures and communities vulnerable to damage and loss during tsunami flooding is the first step towards the development of land use zones, planning regulations, disaster management plans and alike. The PTVAM 2 was developed to do just this. Like all models, its founding assumptions need to be questioned and its various attributes tested or ground-truthed in order to determine their robustness. The Indian Ocean tsunami of December 26, 2004 has provided a valuable opportunity to evaluate the PTVAM. We show that the PTVAM 2 framework stands up well to the damage and losses encountered during a major transoceanic (far-field) tsunami (at least for our field study region). We maintain that the PTVAM 2 framework is widely applicable in areas subject to tsunami inundation. Acknowledgements Aon Re Australia Ltd is thanked for providing the funds for the lead author to participate in the post-tsunami assessment surveys. The Government of the Republic of the Maldives is thanked for its permission to conduct field work in the Maldives and the Island Chiefs for access to their islands. Zaha Waheed of the Marine Research Centre is warmly acknowledged for her invaluable assistance with organising
VALIDATING A TSUNAMI VULNERABILITY ASSESSMENT MODEL
permissions, arranging logistics, her interpretative skills and her valuable discussion in the field. Staff of the National Disaster Management Centre and officials from the Ministry of Finance and Ministry of Planning generously provided great assistance and access to summary data and maps. Professors Barbara Keating and Charles Helsley and the AusAID funded school engineering team are also thanked for valuable discussion in the field. We would like to extend a very special thank you to the people of the Maldives who shared their experience and gave us access to their homes and businesses. We are also grateful to two anomalous referees and the Editor of Natural Hazards for valuable advice on an earlier draft of this paper. References Alexander, D.: 2000, Confronting catastrophe: new perspectives on natural disasters. Terra Publishing, 282 pp. Chang, N.-B., Wei, Y. L., Tseng, C. C., and Kao, C.-Y. J.: 1997, The design of a GIS based decision support system for chemical emergency preparedness and response in an urban environment, Comput. Environ. Urban Syst. 21(1), 67–94. Cheung, K. F., Phadke, A. C., Wei, Y., Rojas, R., Douyere, Y., Martino, C. D., Houston, S. H., Liu, P., Lynett, P. J., Dodd, N., Liao, S., and Nakazaki, E.: 2003, Modelling of storm induced coastal flooding for emergency management, Ocean Eng. 30, 1353–1386. Contini, S., Bellezza, F., Christou, M., and Kirchsteiger, C.: 2000, The use of geographic information systems in major accident risk assessment and management, J. Hazard. Mater. 78, 223–245. Esogbue, A.: 1996, Fuzzy sets modelling and optimization for disaster control systems planning, Fuzzy Sets Syst. 81, 169–183. Fischer, T., Alvarez, M., Dela Llera, J., and Riddell, R.: 2002, An integrated model for earthquake risk assessment of buildings, Eng. Struct. 24, 979–998. Gambolati, G., Teatini, P., and Gonella, M.: 2002, GIS simulations of the inundation risk in the coastal lowlands of the northern Adriatic Sea, Mathem. Comput. Model. 35, 963–972. Jenkins, L.: 2000, Selecting scenarios for environmental disaster planning, Eur. J. Oper. Res. 121, 275–286. Papathoma, M. 2003. Assessing tsunami vulnerability using GIS with special reference to Greece. Unpublished PhD thesis, Coventry University (UK), 290 pp. Papathoma, M. and Dominey-Howes, D.: 2003, Tsunami vulnerability assessment and its implications for coastal hazard analysis and disaster management planning, Gulf of Corinth, Greece, Nat. Hazards Earth Syst. Sci. 3(6), 733–744. Papathoma, M., Dominey-Howes, D., Zong, Y., and Smith, D.: 2003, Assessing tsunami vulnerability, an example from Heraklion, Crete, Nat. Hazards Earth Syst. Sci. 3, 377–389. Pearson, H.: 2005, Scientists seek action to fix Asia’s ravaged ecosystems, Nature 433, p. 94. Peters, J. and Hall, G.: 1999, Assessment of ambulance response performance using a geographic information system, Soc. Sci. Med. 49, 1551–1566. Pidd, M., Silva, F. N.de, and Eglese, R. W.: 1996, A simulation model for emergency evacuation, Eur. J. Oper. Res. 90, 413–419. Tufekci, S.: 1995, An integrated emergency management decision support system for hurricane emergencies, Saf. Sci. 20, 39–48.
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Williams, N.: 2005, Tsunami insight to mangrove value, Curr. Biol. 15(3), R73. Wisner, B., Blaikie, P., Cannon, T. and Davis, I.: 2004, At risk: natural hazards, people’s vulnerability and disasters. Routledge, 2nd edn, 284 pp. http://www.asiantsunamivideos.com [accessed 11th May 2005.]. Zerger, A., Smith, D., Hunter, G., and Jones, S.: 2002, Riding the storm: a comparison of uncertainty modelling techniques for storm surge risk management, Appl. Geogr. 22, 307– 330.