Ocean & Coastal Management 108 (2015) 116e130 Contents lists available at ScienceDirect Ocean & Coastal Management journal homepage: www.elsevier.com/locate/ocecoaman A GIS-based approach for hurricane hazard and vulnerability assessment in the Cayman Islands A. Taramelli a, *, E. Valentini a, 1, S. Sterlacchini b, 2 a b ISPRA Institute for Environmental Protection and Research, via Vitaliano Brancati, 60, Rome, Italy National Research Council of Italy, Institute for the Dynamic of Environmental Processes, Piazza della Scienza, 1, 20126 Milano, Italy a r t i c l e i n f o a b s t r a c t Article history: Available online 2 September 2014 Coastal areas are complex systems that represent the interface between the human, physical and natural components. This paper describes the design, development and application of a conceptual foundation for a quantitative integrated coastal element vulnerability assessment using the up to date Source ePathwayeReceptoreConsequence (SPRC) approach. It is a conceptual model that combines a wellestablished approach in the field of waste and pollution management with the possibility of introducing the concept of system diagrams. Through the implementation of hazard classification, the approach leads to critical facilities identification and the loss estimation for specific hazards when different types of buildings are selected. In the example of Cayman Islands, the presence of exposed elements at risk, as the port or the airport, named critical facilities, drives serious potential damage effects due to high winds and storm surge. This approach provides both a spatial data infrastructure design, for collecting, storing and managing critical facilities information and a vulnerability assessment procedures for structural and operational components, concerning coastal zones affected by hurricane and related hazards. The final part of the paper synthesizes the conceptual treatment of coastal vulnerability in the Grand Cayman Island and underlines the ready-to-use GIS based vulnerability methodologies for risk assessment allowing to build capacity and resilience of the local communities. © 2014 Elsevier Ltd. All rights reserved. 1. Introduction In the last decades, developing hazard models for hurricane impact using GIS have become a major topic of research (Colby et al., 2000; Guzman-Tapia et al., 2005; Frazier et al., 2009; Taramelli et al., 2010; Krishnamurthy et al., 2011). Basic approaches based on Multi-Hazard model method, have been applied to hurricane hazard/elements at risk assessment using GIS data (Boyd et al., 2002; Bausch, 2003; Tran et al., 2009). Indeed, despite the disastrous effects of hurricanes on coastal and inland communities are well known (O'Hare, 2001; Pielke et al., 2003; Watson and Johnson, 2005), there is still a need to better understand how to manage vulnerability to the different mechanisms related to hurricanes strike like storm surges, floods and high winds (Frazier et al., 2010a). Moreover, hurricane vulnerability identification and * Corresponding author. Tel.: þ39 (0) 6 5007 4635; fax: þ39 (0) 6 5007 4912. E-mail addresses: [email protected], [email protected] (A. Taramelli), [email protected] (E. Valentini), simone. [email protected] (S. Sterlacchini). 1 Tel.: þ39 (0) 6 5007 4635; fax: þ39 (0) 6 5007 4912. 2 Tel.: þ39 (0) 26448285; fax: þ39 (0) 264482895. http://dx.doi.org/10.1016/j.ocecoaman.2014.07.021 0964-5691/© 2014 Elsevier Ltd. All rights reserved. prediction of the related risk assessment remain largely unsolved problems (Kok and Winograd, 2002; Pielke et al., 2008). Vulnerability is, in fact, a multidimensional concept associated with high uncertainty in measurement and classification. Developing a vulnerability index from the diverse and often incommensurate data that form the basis of vulnerability assessment is often a core challenge of vulnerability research (Davidson et al., 2007; Eakin and Bojorquez-Tapia, 2008). It is well known that hurricane hazard is controlled by or dependent on a large and complex set of natural and human induced environmental factors (Howard et al., 2003; Shen et al., 2005; Cutter and Emrich, 2006). To complicate matters further, hurricane related components like storm surges, floods and high winds, require forecasting appraisal that is often founded upon different methods, techniques and tools (Landsea et al., 1999; Jiang et al., 2003; Bao et al., 2006; Knutson et al., 2010) that just recently started to deal with local perceptions and transferring risk into policy making (Hallegatte, 2008; Frazier et al., 2010b; Krishnamurthy et al., 2011). The countries of the Caribbean are among the most disaster prone areas in the world by measures such as disaster frequency, population affected and value of damage (Anderson et al., 2010). A. Taramelli et al. / Ocean & Coastal Management 108 (2015) 116e130 In the 52 years from 1950 to 2002, Grand Cayman has experienced seven tropical storms and six hurricanes and the Sister Islands six tropical storms and five hurricanes (Tompkins and Hurlston, 2003; Novelo-Casanova and Su arez, 2010). However, more recently, in 2004 and 2008 Hurricane Ivan and Paloma caused billions of dollars in damage to the economy, environment and infrastructures (Emdat database: http://www.emdat.be/resultcountry-profile; Young, 2004; Young and Gibbs, 2005). With regard to other natural related hazards like earthquake, the Cayman Islands lie in a zone that is close to the boundary of the Caribbean and North American tectonic plates. This transform boundary, where the plates slide past each other, is known to generate earthquakes. The Islands frequently experience minor tremors, though often unnoticed by most residents. On 19 January 2010 the Cayman Islands were hit by an earthquake registering magnitude 5.8 on the Richter Scale, resulting in the issuance of a tsunami watch which was later canceled. Prior to this, the last earthquake to hit the Cayman Islands was magnitude 6.8 on 14 December 2004, occurring just three months after Hurricane Ivan devastated the Cayman Islands (De Mets and Wiggins-Grandison, 2007). Thus, climate change and its natural related hazard impact was the subject of a recent workshop held in Havana, Cuba, as part of a project called The Future of Climate Extremes in the Caribbean (XCUBE e Mesquita et al., 2013). The Caribbean islands are, in fact, characterized by a range of coastal hazards such as earthquakes, tsunamis and most notably, those related to hurricanes. It is thus important to assess the risk posed by these hazards because the islands are environmentally and economically significant due to natural resources, industry, trade and tourism (Birkmann, 2007; Birkmann and Wisner, 2006). Existing coastal management approaches are not always able to adequately address risk, as the vulnerability study is not always included as a management option (Cutter and Emrich, 2006; R3i Contractor Report, 2011; Krishnamurthy et al., 2011). Vulnerability is a key component of risk assessment and it is the capacity of elements exposed to hazards, such as people, resources and infrastructure, to suffer damage (Cova and Church, 1997; Sisson et al., 2006). The Provision of Services to Caribbean Overseas Countries and Territories (OCTs) addresses the risk and exposure of these islands by providing a network of regional infrastructure, programs, policies and protocols to strengthen their capacity to predict and prepare for natural hazards, thereby improving resilience and reducing risk and subsequent loss. Based on the information made available by the R3i project, the efforts of this work are directed to identify which critical facility and resource could be potentially threatened by hazards and to rank the magnitude, frequency and probability of occurrence of the natural and man-made hazards that might potentially affect these structures. Vulnerability to natural hazards, such as hurricanes and related floodings, storm surges, waves and high-speed winds, of the exposed elements can be assessed by the analysis of the different dimensions composing the vulnerability of the physical components and the local communities (Birkmann, 2005, 2006). Because of the need to integrate and manage all these factors and aspects with the development of coastal zone, GIS appears to be the most appropriate tool to deal with those tasks for coastal managers and operators (Andrews et al., 2002; Kienberger, 2007; Rodríguez et al., 2009). A GIS based approach can clearly shows the spatial and temporal evolution of dynamic processes through static maps and matrix of spatial information, as well as the factors that control their behavior in order to analyze the potential scenarios and to evaluate the impact on buildings and manage them properly (Moe et al., 2000; Li et al., 2000; Zhang and Grassle, 2002). Since GIS was one of the tools recommended in Word Coast 117 Conference in 1993 (Vellinga and Klein, 1993), a number of different projects using GIS applications and methodologies for coastal zones have been developed (e.g. BALTICSEA-WEB, Laitinen and Neuvonen, 2001; Zhang and Grassle, 2002; Dune Hazard Assessment Tool, NOAA Coastal Services Center, 2003; Miller et al., 2003; THESEUS, Zanuttigh et al., 2014). Particular is the use in the littoral zone, where GIS allows homogenization and integration of all the available information into geodatabases, a standardized access to data, the generation of thematic cartography and spatial and geostatistical analysis (Pompe and Rinehart, 2008; Debaine and Robin, 2012). This characteristic is especially useful, for example, in the integration and analysis of the indexes used to identify coastal vulnerability (Doukakis, 2005; Debaine and Robin, 2012) and to produce maps for coastal risk analysis. FEMA (Lindell et al., 2006), for example, proposed a probabilistic approach in which different fragility curves for different types of hazardous events and building type are proposed using a GIS platform. Each curve statistically describes the likelihood of a certain building type of exceeding a certain limit state at a specified stress condition. In the present work, the primary objectives are to identify the various critical facilities within the Cayman Islands, determine their level of exposure to natural hazard events and assess the structural and operational dimensions of vulnerability to be used in loss estimation. For this aim, two different approaches have been used: the former based on the calculation of indexes; the latter on the use of available fragility functions for different hazardous events and building types. The use of indexes or fragility functions is a well known approach to vulnerability and risk assessment studies to represent the system's or the community's physical (structural, including the built environment), socio-cultural, socio-economic and environmental susceptibility to damage. In this work, the vulnerability assessment is based on the SourceePathwayeReceptoreConsequence concept (SePeReC) framework widely used in the fields of waste and pollution management and utilized by the UK's Foresight Future Flooding, the EUfunded FLOODsite and the THESEUS projects (FLOODSITE CONSORTIUM, 2009, Narayan et al., 2012, 2014; Kane et al., 2014). The methodology is completely GIS-based and provides a framework within which the case study data collection defines the basic information in a way, which is consistent and compatible with the proposed hazard and vulnerability assessment framework. The applied model thus provides a consistent approach for achieving comprehensive element at risk and vulnerability analysis that are related to the Grand Cayman test case, but could be individual to each coastal areas for all the different Caribbean islands. These are obtained through a robust data structure implementation that represents a model-building exercise to facilitate a shared understanding of the applied system within different hazard management office in the whole area. The model is a powerful tool for structuring and integrating existing knowledge across multiple island reality with different management approach. Applications of the GIS model provide key insights into the characteristics of complex coastal areas d insights that will inform the quantification process linked to hurricane related events leading to a shared coastal spatial planning for hurricane vulnerability analysis. 1.1. Study site Located in the western Caribbean Sea (Fig. 1a) at the northwest of Jamaica, the Cayman Islands (CI's) are a British overseas territory comprised of three islands: Grand Cayman (GC, Fig. 1b), Cayman Brac (CB, Fig. 1c), and Little Cayman (LC, Fig. 1d). These three islands occupy around 250 kmq of land area (Brunt and Davies, 1994). 118 A. Taramelli et al. / Ocean & Coastal Management 108 (2015) 116e130 Fig. 1. a) Overview of the Caribbean Sea and the surroundings islands. The Cayman Islands are a British Overseas Territory considered part of the geographic Western Caribbean Zone as well as the Greater Antilles. The Caymans territory comprises three islands: b) Grand Cayman, c) Cayman Brac and d) Little Cayman represented in the figure by the ground elevation layer (Digital Terrain Model) provided by Land and Survey Department, Grand Cayman. GC is approximately 115 000 ft long and 43 000 ft wide at the widest point. The highest elevation is about 60 ft above sea level and the most striking geographical feature is the North Sound, a shallow reef-protected lagoon with an area of about 602 780 000 ftq. CB lies about 475 000 ft northeast of GC. It is about 62 300 ft long and a little over 5 300 ft wide. LC is 26 200 ft west of CB and is 52 500 ft long and 9 800 ft at its widest point. It is the flattest of the three islands with its highest elevation being 39 ft. To the west, a 36 000 ft channel separates CB from LC (Brunt and Davies, 1994). The three islands are mostly flat (Fig. 1bed) and were formed by large coral heads, covering submerged ice age peaks of western extensions of the Cuban Sierra Maestra range. The highest point is The Bluff, a limestone outcrop 140 ft in height on the eastern end of eastern CB. The CI's lowest elevation is the Caribbean Sea at sea level (Brunt and Davies, 1994). Climate in the GC basin can be classified as dry-winter tropical, with significant subregional variations in rainfall annual totals, length of the rainy season, and timing of rainfall maxima. The climatologic (1951e80) annual mean rainfall, averaged over all the 188 stations (Giannini et al., 2000; White et al., 2004), is 5 ft per year. Three rainfall regimes can be related to the geography of the Caribbean-Central American region. A MayeOctober rainfall regime is typical of the Central American. In this context rainfallbearing disturbances, known as African easterly waves (Riehl, 1954; Burpee, 1972), propagate across the Atlantic Ocean into the Caribbean basin from mid June to early October generating hurricanes. So that the islands are subject to numerous hazards, most notable are those related to hurricanes (Emanuel, 2005). Worldwide, approximately 85 percent of direct losses from natural hazards are related to hurricane events (Gall et al., 2011). A famous example for Cayman Islands was Hurricane Ivan, which occurred September 11th, 2004. When Ivan reached the islands, it was Category 5 strength on the SaffireSimpson Hurricane Scale, creating an 8 foot storm surge on Grand Cayman. An estimated 83% of the buildings were damaged, 4% of which required complete reconstruction, with the majority of damage due to flooding and wind. Power, water and communications were disrupted for months in some areas and Ivan was the worst hurricane to hit the islands in 86 years (Thompson, 2010). According to the United Nations Economic Commission for Latin American and the Caribbean (UNECLAC, 2004), the Cayman islands were impacted more than any other islands in the Caribbean in terms of economic loss after the 2004 hurricane season (Novelo-Casanova and Su arez, 2010). The paths of tropical storms and hurricanes that passed within 96 km of the CI's since 1853 are exposed in the UNECLAC report (2004). The number of tropical systems passing nears the CI's at 5-year intervals (from Caribbean Hurricane Network: www. stormcarib.com) from 1851 to 2006 shows that most storms occurred from 1930 to 1934and the most severe hurricanes were registered between 1915 and 1919. On average, the CI's are affected, brushed or hit by hurricanes every 2.23 years. The average number of years between direct hurricane hits (usually within 64 km to include small hurricanes) is once every 9.06 years. The months of September, October, and November are typically the most active for rez, 2010). hurricanes in the islands (Novelo-Casanova and Sua During these months, storms tend to form in the southern Caribbean and move north, into or close to CI's. During the 55 years from 1950 to 2004, GC experienced seven tropical storms and seven hurricanes, and CB and LC six tropical storms and five hurricanes. A. Taramelli et al. / Ocean & Coastal Management 108 (2015) 116e130 The most important hurricanes that have directly impacted the CI's in recent years, in addition to Ivan, are (Novelo-Casanova and Su arez, 2010): Gilbert, September 1988, Mitch, October 1998, Michelle, November 2001, Wilma, October 2005 and Dean, August 2007. 2. Material and methods The methodology was implemented using the available dataset (R3i Contractor Report, 2011). The Hazard Management Cayman Islands (HMCI), the Lands Survey Department (LSD) Cayman Islands and the Department of Environment Cayman Islands generously provided part of this information under the R3i funded project. A limitation of the data used that are the most update ones, is the dating involved in model application, specifically with regard to the data used to build it. However, this problem is not seen as essential as it ensures that the model can be built commensurate to the amount of data and time available for the new data acquisition. Moreover, the resulting conceptual model of the coastal vulnerability analysis explicitly reflects these limitations due to the fact that the process of model construction is universal and equally applicable to all sites, though the resultant model is built to deal with the diverse characteristics of each coastal area where the model based on Cayman has to be applied further. The data availability is: Cadastre Map of the Cayman Islands (Lands and Survey Department, Cayman Islands, 2007) Cayman Islands' National Hurricane Plan 2006 (Emergency Operation Centre, Cayman Islands, 2006) Development Plan Map 2006 (Central Planning Authority, Cayman Islands, 2006) Map of flooding areas during Ivan Hurricane (Department of Environment, Cayman Islands, 2005) Map of Ivan Hurricane Preliminary Damage Assessment (Lands and Survey Department, Cayman Islands, 2005) Map of location (latitude and longitude) of critical facilities (hospitals, schools, shelters, fuel deposits, fuel and gas pipeline, government communications infrastructure, power stations, ports, water and sewage treatment plants, water storage plants, airport, police and fire departments, critical government, and Red Cross installations) (Hazard Management Cayman Islands, 2009) Petroleum Products Location Map (Lands and Survey Department, Cayman Islands, 2007) Preliminary Post-Ivan Environmental Impact Assessment Report (Department of Environment, Cayman Islands, 2004) Quikbird acquisition on the tree islands (Department of Environment, Cayman Islands, 2006) Terrain models, Grand Cayman, Cayman Islands (Lands and Survey Department, Cayman Islands, 2007) EMMA hazard maps based on TAOS approach (Hazard Management Cayman Islands, 2009) The buildings and facilities analyzed are as follows: Buildings are grouped together into specific building types and occupancy classes following HAZUS-MH classification (FEMA, Bausch, 2003). Degrees of damage and loss are computed for each group. Building types (Table 1) are classified according to: the number of storeys, usage and construction material and techniques (for example: one-storey wood frame residential buildings, twostoreys masonry multi-family residential buildings, low-rise masonry strip mall buildings, etc.). Each model building type is further defined by a distribution of wind building characteristics, such as: roof shape, roof covering and opening protection. Three 119 Table 1 Construction categories used to classify the critical facilities surveyed in Grand Cayman. General building type Construction description Wood Masonry Steel Concrete Manufactures homes Wood frame Reinforced or un-reinforced masonry Steel frame Cast-in-place or pre-cast reinforced concrete Factory-built residential construction predominant roof shapes are modeled: Hip, Gable or Flat. For flat roofs, two roof coverings (Built-Up Roof or Single Ply Membrane) and three roof-covering conditions (New, Good or Poor) are considered. For all roof shapes, two roof-sheathing fastener conditions (6-penny nails or 8-penny nails) and two roofewall connection conditions (Strapped or Toe-Nailed) are modeled. A significant feature is the possibility to model the benefits of mitigation for all building types. The mitigation options available are: (1) strengthened roofewall connections (i.e., straps or clips instead of simple toe-nailed connections), (2) upgraded roof sheathing attachments, (3) pressure and impact resistant protection for all openings, and (4) secondary water resistance to prevent water penetration through the roof decking after the loss of the roof covering. Examples of occupancy classes are single-family dwelling, retail trade, industry, etc. Due to the variations in building type and performance, the model building types used are designed to represent the average characteristics of buildings in a class (HAZUSMH, FEMA, Bausch, 2003). That is, the damage and loss prediction models are developed for model building types and the estimated performance is based upon the “average characteristics” of the total population of buildings within each class. Within the different type of building we also considered for the operational vulnerability what is defined as an Essential Facilities: this includes medical care facilities, emergency response facilities (fire stations, police stations, etc.) and schools, that are those vital to emergency response and recovery following a disaster. School buildings are included in this category because of the key-role they often play in housing people displaced from damaged homes. Generally, there are very few of each type of essential facilities in a census tract, making it easier to obtain site-specific information for each facility. Damage and loss-of-function are evaluated on a building-by-building basis for this class of structures, taking into account the construction and structural characteristics (model building type) of the facility under analysis, even though the uncertainty in each estimate could be large. Utility lifeline systems, includes potable water, gas and electric power, waste water, communications and liquid fuels (oil and gas). Examples of components are electrical substations, water treatment plants, tank farms and pumping stations. The SPRC (Fig. 2) is a model for representing systems and processes that lead to a particular consequence. The SPRC model describes the hazards in terms of the process of event propagation e the initiation of a hazard and its propagation through a pathway to a receptor with particular (negative) consequences (Hallegatte et al., 2013). The model was first used in the environmental sciences to describe the movement of a pollutant from a source, through a conducting pathway to a potential receptor (Holdgate, 1979) and was first adapted for coastal flooding in the UK by the Foresight: Future Flooding study (Evans et al., 2006). Several new examples were then applied using the approach (Hanson, 2010; Hanson and Nicholls, 2012; Narayan et al., 2014). For a risk to arise, there must be hazard that consists of ‘the source’, an individual or object that could be damaged ‘the receptor’; and a 120 A. Taramelli et al. / Ocean & Coastal Management 108 (2015) 116e130 Fig. 2. The SourceePathwayeReceptoreConsequence (SPRC) approach (Mendez, 2011 e Personal Communication). ‘pathway’ between the source and the receptor. Essentially, the system is defined by identifying known route(s) for Pathway(s) between their origin (Source) and impacts (Receptor). Once the sourceepathereceptor is identified, an impact can be evaluated as a consequence. The impact can cause social, economic or environmental damage that results from the hazard and is generally expressed quantitatively, qualitatively or as categories (i.e. High, Medium, and Low). There may be a number of sources, areas affected (receptors), type and nature of impacts and linking pathways, but an important aspect of the concept is that there must be a pathway in order for a receptor, consequence, and therefore risk, to exist. The purpose of SPRC is to provide (i) a clear definition of the hazard system and (ii) a conceptual map showing the inherent, causal relationships and interdependencies that will need to be represented in the analytical vulnerability assessment framework. Once the approach is defined, the elements at risk (people, infrastructure, land cover, activities, public and private services, etc.) are collected, validated and stored in a database using a GIS system (Meyer, 2007). The geometric features (shapes, perimeters, areas and, eventually, volumes) and the relevant descriptive attributes (occupancy rates of buildings, type of door, type of windows, type of roof, etc.) of the elements at risks are collected and stored. A catalog of historical damage and estimates of losses is also stored in the database; information can come from local archives, insurance companies, and interviews of owners' elements, which have been damaged. The physical and environmental components of vulnerability can be defined as the degree of loss due to the exposure (loss) of human settlements to a hazard and the likelihood of being affected by dangerous phenomena due to the location and physical conditions of elements that will sustain certain hazard impacts (Fig. 3). The analysis includes not only the critical facilities but also the community distribution. At a first instance, indicators should include the age, the gender, if inhabitants are employed or unemployed, the occupation depending upon whether skilled or unskilled, also linked to income and financial status, as well as the educational level (higher or lower educational level). Then, emphasis should be put on the family size and composition of households e.g. large family, single person household, number of dependents, if single parent, the household income, tenure status (owner, renter), disability, nationality/ethnicity (non-white, new immigrant), the years of residency and whether there is participation in social networks. Apart from persons and/or households, it is important also to explore firms and economic production at risk through the use of input data that identify type and size of firms, number of employees, financial data (profit, sales) etc. Hence, the aim for element-at-risk indicators (Fig. 4) is to specify the amount of social, economic or ecological units or systems which are at risk of being affected regarding the relevant kinds of hazards in a specific area, e.g. persons, households, firms, economic production, private and public buildings, public infrastructure, cultural assets, ecological species and landscapes located in a hazardous area or connected to it (Meyer and Messner, 2006). As a result, the vulnerability contribution is to map which and how many elements are at risk of being affected by hazardous events. In that way, the magnitude of damage can be estimated in monetary and non-monetary units. As Meyer and Messner (2006) note since “every element at risk is more or less exposed to events and more or less susceptible to them, exposure and susceptibility indicators are related to element-at-risk indicators and contribute significantly to the analysis of vulnerability”. The methodology (Fig. 4) aims to develop a systematic approach to coastal vulnerability providing both spatial data infrastructure for collecting, storing and managing critical facilities information and procedures to assess vulnerability. A range of analysis composes the methodology, including: elements at risk, loss estimation (based on the use of vulnerability curves) and operational vulnerability to quantify the impact of extreme events (Fig. 5). The vulnerability assessment developed can be summarized in a step by step procedure as follows: Spatial data infrastructure design and implementation Collection of individual hazard maps Categorization of hazards A. Taramelli et al. / Ocean & Coastal Management 108 (2015) 116e130 121 Fig. 3. A fragility curve demands knowledge on the physical parameters of hazard, the structural characteristics of critical building components and the terrain environments. In the specific case of hurricane, the physical parameter of hazard is represented by the maximum peak gust speed measured at 32 ft above the terrain level; the structural characteristics of critical building components by the type of buildings (engineered residential steel building in this example), the number of storey (two-storey), the type of roof (built-up roof cover) and the glazing coverage (33%); the terrain environments by the terrain exposure (z0 ¼ 0.1 ft open terrain) and the windborne debris model (missile environment D ¼ no windborne debris). For a pre-defined peak gust speed (a), four different damage states may be simulated in probability terms. External tables (b) explain the extent and severity of damage to structural and non-structural components of a building type. All the fragility curves and descriptions concerning types of building and damage state functions have been extracted from HAZUS-MH MR4 Technical Manual and User Manual. Creation of an inventory of critical facilities Categorization of critical facilities Elements at risk exposure to hazards calculation Structural vulnerability assessment for identified hazards and critical facilities Loss estimation by fragility curves selection Operational vulnerability assessment for identified hazards and critical facilities The spatial data infrastructure was implemented within ArcMap 9.3 provided by ESRI®, with a set of tools for collecting, storing, retrieving, transforming, and displaying spatial data from the real world for a particular set of purposes (Burrough, 1986) and is, therefore, appropriate for this type of analysis since the vulnerability changes with respect to several parameters like the location. The geophysical and structural data are stored as raster and vector maps, including EO imagery and classification, as well as shape files obtained by in situ data acquisition (Fig. 6). Government offices such as the Hazard Management Cayman Islands, the Land and Survey Office and the Department of Environment of Grand Cayman provided images and data in geographic (WGS84) or projected (NAD27 Grand Cayman) coordinate system. All data were processed in the original projection setting. HURRICANE RELATED HAZARD Geodatabase Based on the above, hurricane related-hazard maps of high winds, storm surges and floods (with information on the magnitude and return period of each hazardous event have been collected and stored in the Geodatabase. A collection of past events was considered a primary source of information. To this end, the USAID/OAS Caribbean Disaster Mitigation Project (CDMP) has supported the development of TAOS/L, a storm hazard model for use in the Caribbean to assess the impact of storm surge and wave action on coastal areas through the region and the exposure of the elements at risk. Storm Surge Raster maps provide the expected level of surge, generally expressed in feet, with no information about velocity as the waves data. Rainfalls are collected by literature review and are considered as an adjunct factor to flooding simulations. ELEMENTS AT RISK geodatabase 122 A. Taramelli et al. / Ocean & Coastal Management 108 (2015) 116e130 ECOLOGICAL and GEOPHYSICAL geodatabase The Ecological and Geophysical geodatabase contains multispectral satellite imagery and the natural resources maps like land covers, natural protected areas, vegetation distributions, wetlands and water lens, etc. Eco-geophysical settings are stored in order to crosscheck results and, if necessary, provide important boundary conditions for the analysis. The three geodatabases implemented are used in the methodological steps for vulnerability assessment concerning different coastal zones: Step 1: hazard assessment (model multiple hazards) Step 2: elements at risk (critical facilities, people, land cover and relevant attributes) Step 3: vulnerability analysis (loss estimation, operational vulnerability) 2.1. Step 1: hazard assessment: source estimation Fig. 4. Example of community distribution vulnerability curves for three typologies of land use. The damage function can be built using the hazard values with terrain environments (i.e. Land use) to delineate the affected area. Based on the monetary values for each class (GDP Damage), an overall damage cost can be calculated as well as the structural characteristics of critical buildings. The elements at risk geodatabase stores a lot of information concerning: government civic and critical facilities, population density, infrastructure, land cover, business activities, public and private services. At a building level, geometric features, perimeters, areas and, eventually, volumes and other relevant descriptive attributes, such as occupancy rates of buildings, have been collected and stored. It also contains information regarding the geographical position and structural characteristics of each building; a catalog of historical damage and estimates of losses; information from local archives, insurance companies and interviews of damaged owners. Systematic in situ surveys and measures integrate the degree of data completeness. The Caribbean Disaster Mitigation Project supported the development of The Arbiter of Storms (TAOS/L), a storm hazard model that can be used to assess the impact of hurricanes on coastal areas in the Caribbean region (Vermeiren and Watson, 1994). TAOS is a computer-based numerical model that produces estimates of water surge height, wave height at the coastline, and maximum sustained winds at the surface for any coastal area in the Caribbean basin. Bathymetric and topographic data of the Cayman Islands is also included in the model to ensure that the TAOS predictions are as accurate as possible. Model runs are made for any historical storm (TAOS/L includes and uses a database of historical storms from 1886 to the present in the Caribbean), for probable maximum events associated with different return periods, or using real-time tropical storm forecasts from the US National Hurricane Center (Iman et al., 2005a,b). Results of individual storm runs can be displayed as raster maps showing the maximum effect (envelope) of the hazards at each location over the entire course of the storm. Rainfall data are not modeled by TAOS but collected by literature review and are considered as an adjunct factor to flooding simulation simply adding the value to the raster map within the GIS. To determine the level of exposure of the elements at risk to hazards, storms, waves and winds are extracted from the atlas. Then, continuous data have been classified on the base of threshold values proposed in the Saffir Simpson classification system (Table 2). Wind raster maps were based on wind velocity and, recently, the National Hurricane Center added the effects of storm surge and flooding to this scale. Authors have cited that surge and flooding are dependent on other factors, such as the size of the storm and the location, as opposed to a hurricane category based on wind velocity (Kantha, 2006). At the beginning, areas potentially affected by high-speed winds, storm surges and floods (waves and rainfall) have been identified in each hazard raster map available in this study (Fig. 7). In the analysis, a SaffireSimpson Category III (111e130 mph) hurricane, crossing Grand Cayman from the southeast approximately 120 (Cayman MET office), has been extracted from the atlas. 2.2. Step 2: elements at risk: pathway and receptor estimation Fig. 5. Conceptual base methodology applied in the Cayman Islands case study. The five broad hurricane intensity categories are used to quantitatively provide the levels of exposure of each element at risk stored in the geodatabase and attain an indication of the potential damage upon landfall. By overlapping hazard maps with elements at risk distribution map, a corresponding hazard class is assigned to each building. This represents the Pathway estimation and is done in order to define the magnitude of different damaging hurricane- A. Taramelli et al. / Ocean & Coastal Management 108 (2015) 116e130 123 Fig. 6. Critical facilities geodatabases implementation. related events assigned to each element at risk (that represent the Receptor estimation). In case of high speed winds: - 2 Critical Facilities fall in class 1 (00.0e94.0 mph) - 21 Critical Facilities fall in class 2 (95.0e110.0 mph) - 39 Critical Facilities fall in class 3 (111.0e130.00 mph) In case of storm surge, all Critical Facilities located on Grand Cayman fall within the class 1 (0.0e5.0 ft storm surge height) and only George Town port is in class 3 (9e12 ft storm surge height). 2.3. Step 3: vulnerability analysis: consequences estimation As a next step, the physical parameters of the damaging events (wind speed and high of water classes) have been related to the structural parameters concerning each element at risk in order to analyze their response capacity (potential degree of loss) against hazards, applying two different approaches: 1. The heuristic approach, based on a simple weight assignment procedure (structural vulnerability). 2. The probabilistic approach, based on the use of fragility functions (loss estimation). In the heuristic approach, a Total Score is calculated by summing up all the weights assigned to each structural and architectural feature composing a critical facility. Total Score shows different Table 2 SaffireSimpson scale referring to the hurricane category, wind speed in miles per hour (mph) and storm surge and wave height in feet (note: wave height was not originally included in SaffireSimpson scale). Between brackets: number of critical facilities surveyed in Grand Cayman and affected by each simulated wind speed, storm surge and wave class (hurricane category: 3; wind direction: 120 ). Hurricane category Wind speed (mph) Storm surge (ft) Waves (ft) 1 2 3 4 5 75e95 (2) 96e110 (21) 111e130 (39) 131e155 (0) >155 (0) 4.0e5.0 (61) 6.0e8.0 (0) 9.0e12.0 (1) 13.0e18.0 (0) >18.0 (0) 4.0e5.0 (60) 6.0e8.0 (2) 9.0e12.0 (0) 13.0e18.0 (0) >18.0 (0) (weak) (moderate) (strong) (very strong) (devastating) response capacity against hurricane winds: from 0 “no or very low response capacity” to5 “very high response capacity”. Structural features include material type, structural components, building codes, and maintenance/retrofitting works. Architectural features include roof type, window type, door type, and glass type. Weights are assigned to each feature and are determined from literature. A calculation of the total sum of the single weights can then be compared to the wind velocity at each critical facility (Table 3). These total sums represent the response capacities against highspeed winds, where low values indicate low response capacity and high values correspond to high response capacity. Concerning the probabilistic approach, vulnerability or fragility functions and curves have been applied to the “most vulnerable Critical Facilities” as resulted from heuristic approach (although the methodology can be easily applied to all critical facilities). In this study, HAZUS-MH (FEMA, Bausch, 2003) has been used to predict the physical damage for different model building types due to wind-induced pressure. For storm surge, the response capacity is usually based on two hazard scenarios according to the U.S. Army Corps of Engineers (USACE, 1985, 1996): 1) Inundation, where only depth of water is considered, and 2) Flooding, where the depth and velocity of water is considered. The purpose of separating the two scenarios is because the resulting damage will be significantly different. For example, it is unlikely that a building will suffer structural failure during inundation; however, if waters flow at a high velocity it is more likely that the building will suffer structural failure because the structure and the foundation may become separated (USACE, 1996). In both cases, the structural finishes and contents may be severely damaged. In our study only the first case is considered due to the data availability (Table 4). 3. Results Here we present the results from the application of the methodology for inland flooding and high speed winds from hurricane. The methodology presented is strongly based on Multi-hazard Loss Estimation Methodology (HAZUS-MH project e Vickery et al., 2006). All the fragility curves and descriptions concerning types 124 A. Taramelli et al. / Ocean & Coastal Management 108 (2015) 116e130 Fig. 7. Information layers concerning hazardous events (derived from EMMA-TAOS project) potentially affecting the island of Grand Cayman. In each map, the color of the dots refers to the hazard class which each critical facility falls in. a) Qickbird 2006 with 60 cm of resolution and, hazard maps classified following the SaffireSimpson scale in terms of b) wind speed in miles per hour (mph), c) storm surge and d) wave height in feet (as listed in Table 2). An additive effect of e) storm surge and wave height and f) storm surge, wave height and rainfall is also proposed. of building and damage state functions have been extracted from HAZUS-MH MR4 Technical Manual and User Manual. The results are divided and presented for hazards related to inland flooding and winds. Within the two subparagraphs the results are discussed following the 3 steps approach. 3.1. Inland flooding 3.1.1. Step1 Hazard classes for storm surge, waves and rainfall on the base of the level in feet of inland flooding raster maps have been assigned to critical facilities weighed on the base of their structural status. 3.1.2. Step 2 Properly, for storm surge height all critical facilities in Grand Cayman fall in class 1 (0.0e5.0 ft wave height) and 1 in class 3 (6.0e8.0 ft wave height). The only one in class 3 is the George Town Port that actually represents one of the major sources of goods for the islands. The functional role of the only one in Class 4 can compromise a lot of abilities. The analysis was specifically designed to obtain the selection of critical facilities on the base of the structural characteristics of each building/facility for considering in a second part of the analysis the government buildings. Hazard effects from waves highlighted that 60 Critical Facilities in Grand Cayman fall in class 1 (0.0e5.0 ft wave height) and 2 are in class 2 (6.0e8.0 ft wave height). The Chevron fuels at the airport and the Prospect Primary School represents the most vulnerable buildings during an inundation of more than 5 ft of water height. When storm surge and waves effect are coupled, 57 Critical Facilities in Grand Cayman fall within the class 1 (0.0e5.0 ft) and 2 critical facilities falls in the class 3 (>9 ft), the Chevron fuels airport and the George Town Port. In the case of inland flooding from hurricane, the two main sources of goods would be compromised by the inaccessibility due to water height. In the framework of a worst-case scenario, considering the concurrent hazard effects of storm surge, waves and rainfall (about 12 in z1 ft of rainfall has been included in the analysis, experienced during Ivan Hurricane, 2004), 55 Critical Facilities located on Grand Cayman fall within A. Taramelli et al. / Ocean & Coastal Management 108 (2015) 116e130 Table 3 Tables of weights assigned to each structural and architectural feature composing critical facilities surveyed in Grand Cayman and exposed to highspeed winds. Material type Weight Wood Masonry Concrete Metal Steel 1 2 3 4 5 Build status Weight Very bad Bad Average Good Very good 1 2 3 4 5 Roof material Weight Clay tiles Concrete tiles Fiber cement slates Shingles Concrete Standing seam Steel 2 3 3 3 5 5 5 Roof Weight Complex Gable Gambrel Hip Flat 2 3 3 4 5 Shutters Weight No shutters Ply wood Accordian Awning Bahamas Steel removable 0 2 4 4 5 Windows Weight Awning windows Single panel Multi panel Miami Hurricane proof glass 1 1 1 1 3 Door Weight Wood Glass Metal 1 2 3 the first class (0.0e5.0 ft), 3 are in class 2 (6.0e8.0 ft), 2 are in class 3 (9.0e12.0 ft). The Caribbean Utilities Company, the Cayman Islands Environmental Centre, the Owen Roberts Airport Facility, the Chevron fuels can be considered elements at low risk less threatened than the Prospect Primary school. The George Town Port is also involved in the inundation; it appears with higher weight in all the analysis. Particular is the weight assumed by airport commercial services because the airport is positioned in a low lying area and the operational capabilities during the flooding for goods and resources provision can be threatened. When considering a local scale scenario, focused on the George Town Port, 54 critical facilities are not affected by surge, while 23 critical facilities are affected by a Class 1 surge. Surge does not exceed 8 ft anywhere in the district and only one critical facility resides in this class, the Airport is subject to a 5 ft surge. When the analysis includes not only the critical facilities but also the community distribution, the values of weighed analysis result higher (Meyer and Messner, 2006). This is because the 125 Table 4 Tables of weights assigned to each structural and architectural feature composing critical facilities surveyed in Grand Cayman and exposed to flooding. Building materials Weight Reinforced concrete structures Cold-formed steel houses High strength steel structures Wood light-frame structures Reinforced masonry structures Unreinforced masonry structures Adobe structures 5 4 4 3 3 2 1 House upward of structural elements Weight Sum of the following items Compacted earth filled of gravel Lifted windows New concrete slab floor Trench and tunnels under the slab 0e5 0 (no) 0 (no) 0 (no) 0 (no) or or or or 1 1 1 1 (yes) (yes) (yes) (yes) Foundations and foundations walls Weight Treated wood 1 Retaining wall foundation Solid clay masonry Fully grouted concrete masonry 2 3 Concrete foundations Pile foundations 4 5 Height of the first floor Weight House and slab floor lifted together Top lowest floor Lowest floor 1 2 3 Height of the first floor Weight House and slab floor lifted together Top lowest floor Lowest floor 3 2 1 Number of storeys Weight 1 Store 2 Storeys >2 Storeys (with lower area converted to not habitable) 1 2 2 Soil types Weight Gravel and gravelly soils Sand and sandy soils Silts and clay Highly organic soils 5 4 3 2 Secure connection of structural elements Weight Elements anchored each other Elements anchored to foundation 1 3 Openings for the entryeexit of flood waters Weight Openings in the foundation lesser of 20% of the total floor Openings in the floor do not exceed 10% of the floor 3 1 communities analysis takes the maximum values of surge from anywhere within the community while the critical facilities analysis takes the value of surge at each critical facilities location (Fig. 8). Surge classes were assigned to each community on the base of the maximum level of its effect in ft. The entire community takes only one surge class value and the maximum surge experience at any location within the community: 9 communities do not experience any surge, 52 communities experience Class 1 surge and 25 communities experience Class 2 surge. No communities experience Class 3 surge. Comparing the two analyses, critical facilities analysis indicates lower vulnerability because the majority of buildings are in the “no surge” category while the communities' analysis indicates that 25 communities are in the Class 2. This comparison illustrates the various and different ways that spatial data can be processed and 126 A. Taramelli et al. / Ocean & Coastal Management 108 (2015) 116e130 Fig. 8. a) Critical facilities plotted on top of storm surge of Hurricane Ivan TAOS model. In the George Town district surge does not exceed 8 ft (Class 2). b) Overlap of Storm Surge (Hurricane Ivan TAOS model) and Communities in George Town District, Grand Cayman. Communities of George Town district are separated by gray lines. Maximum surge values that fall within each community are assigned to each community and distinguished by color. No communities fall into surge Class 3. how the results can vary. Based on the above, the key layers (hazard) in the GIS elements at risk model are the hurricane relatedhazard maps of high winds and the inland flooding with information on the magnitude and return period of each hazardous event. 3.1.3. Step 3 Different hazard scenarios and related consequences have been investigated, the inundation represented by the depth of water and the flooding that considers only depth, are used to evaluate the degree of loss by applying Depth-Damage curves. Flood fragility functions were related to the depth of water (in feet) and measured from the top of the first finished floor and they express damage as a percent of replacement cost, namely loss estimation. The model of single familiar residential structure depth-damage curves adapted to the one-storey buildings, two-storey buildings and split level buildings with/without basement show the effect for increasing values of water height. Curves are available for six structure categories: 1) one floor, no basement, 2) two or more floors, no basement, 3) two or more floors, with basement, 4) split-level, no basement, 5) split-level, with basement and 6) mobile home (FIA Credibility and Weighting report, 1998) (Fig. 9). Damage curves were applied to single buildings and they were reliable as predictors of damage for large population groups. In the case of inundation, fragility functions are also known as depthdamage curves and for inundation no structural damages were considered. Flooding with significant depth can result in structure and content damage in addition to the damage caused by simple inundation. In the Cayman Islands it appears a large distribution of wooden and concrete made buildings. Velocity-based building collapse curves developed by the Portland District of the U.S. Army Corps of Engineers have been utilized. These curves relate collapse Fig. 9. Credibility weighted depth-damage (CWDD) curves. Inundation depth-damage curves (riverine) for six building types developed by FIA, showing structural and contents losses (% replacement costs) as a function of water depth (ft). A. Taramelli et al. / Ocean & Coastal Management 108 (2015) 116e130 potential to overbank velocity (in ft per second) and water depth (in ft) for three building material classes (wood frame, steel frame and masonry or concrete bearing wall structures). The Portland collapse curves for wood, masonry and steel frame can be associated to the critical facility selection and evaluate the weighed analysis on the base of historical records. 127 Table 5 Model building type and number of critical facilities belonging to each model building type surveyed in Grand Cayman. Model building type Nr Wood and masonry (unreinforced and reinforced) Metal Concrete and steel 1 3 71 3.2. Winds 3.2.1. Step 1 Hazard classes for high winds on the base of the wind velocity raster maps have been assigned to critical facilities weighted on the base of their structural status. In case of high-speed winds, the minimum weight value was 0, meaning “no or very low response capacity” to high wind events; on the contrary, a value of 5 means “high response capacity” from the critical facility under analysis against high winds. 3.2.2. Step 2 The Total Score ranges between 8 and 31 calling for different response capacity against high winds and low Total Score values may refer both to real low response capacity from the critical facility or missing data (Fig. 10). 2 Critical Facilities fall in class 1 (00.0e94.0 mph); 21 Critical Facilities fall in class 2 (95.0e110.0 mph); 39 Critical Facilities fall in class 3 (111.0e130.00 mph). The land base maps of the area highlights spatial boundary condition of each group of critical buildings that are represented by natural and managed areas. George Town Port, the Cayman Islands Hospital, two the North and West Bay Police stations show the distribution of the essential critical facilities for the operation vulnerability analysis. In the operational vulnerability analysis the gas and fuel terminals as well as health centers are weighted with lower values. 3.2.3. Step 3 For high wind velocity the element at risk analysis provided the critical facility selection for damage functions, named fragility functions. From this probabilistic approach, the physical damage model predicts wind-induced pressure for different Model Building Types on the base of the available database. In this work we selected the http://www.fema.gov/plan/prevent/hazus/. The effect of the actual local terrain (z0) is then taken into account by modifying that wind speed by a factor, which is dictated by the exposure category for the local terrain. Based on the FEMA probabilistic approach, examples of specific building types include one-story wood frame residential buildings, two-story masonry multi-family residential buildings, or low-rise masonry strip mall buildings, etc. These fragility curves were based on physical parameters of hurricane, environmental parameters, structural and architectural characteristics of building components (Table 5). So far, the parameters that have been investigated are: 1) hazard including wind velocity and hurricane return period 2) ground surface roughness (z0), 3) specific building types and occupancy classes, 4) nail type, 5) roof type, 6) window type. Each of these controls the choice of the proper the fragility curve. The probabilistic phase of the vulnerability analysis is based on the model building types for wood and masonry materials. Nail length, roof and window models are considered. The case of major significance comes from CB, where the electric plantation results with a major weight. An example is the residential masonry class (reinforced or unreinforced) for four different damage states ranging from 1 (minor damage) to 4 (severe damage. The structure has two storeys, 6d roof sheathing nails, strapped roof trusses, gable roof, no garage, and wood frame walls. A sensitivity test comparing different z0 values shows the effects of surface roughness on damages for the same building class (Table 6). We compare damage curves when z0 ¼ 0.1 ft (open terrain) and z0 ¼ 3.30 ft (suburban terrain). 75 critical facilities fall within this model building type: 71 critical facilities are made by concrete and 4 critical facilities are made by steel. The damage probabilities for each building class are estimated with its probability of failure or the probability that the wind load effect (e.g., aerodynamic pressure or impact energy) is greater than the resistance of the element. 4. Discussion This paper summarizes a collective effort to understand and develop methods to mitigate risk in the Caribbean area, focusing on vulnerability assessment in the Cayman Islands due to hurricane- Fig. 10. a) Critical facilities plotted on top of High winds of Hurricane Ivan TAOS model and b) Hurricane Ivan surge as it was modeled by the Environmental Department of Caymans under the Critical Facilities status, the majority of buildings are in very good and good status also in lower coastal areas. 128 A. Taramelli et al. / Ocean & Coastal Management 108 (2015) 116e130 Table 6 Critical facilities surveyed in Grand Cayman and belonging to the model building type “metal aluminumeiron”. Category Description Building type Electric plants Government building Seaport Caribbean utilities company Custom's warehouse Cargo distribution center Metal aluminumeiron Metal aluminumeiron Metal aluminumeiron related hazards. This work is GIS-based and follows three main steps: 1) Hazard scenarios definition (type, magnitude, return period of different damaging events); 2) Elements at risk identification (type, location, structural and architectural features), exposure of elements at risk to hazards; 3) Vulnerability assessment. Hazard assessment includes modeling the hurricane-related hazards that could affect elements at risk in order to estimate the Source of the approach. The Pathway and consequences estimation is based on the elements at risk calculation and on the location and the structural components of the different buildings. Finally the loss estimation is performed as a separate estimate of the probability of loss via fragility functions, which are a necessary step forward in quantitative risk assessment. By assessing the exposure to define elements at risk and the critical facilities included, the example of the storm surge in the district of George Town and the spatial extent of surge with critical facilities and communities, shows how vulnerability varies geographically and the importance of performing these types of assessments with GIS. The hurricane loss estimation methodology is designed to produce loss estimates for use by state, regional and local governments in planning for hurricane risk mitigation, emergency preparedness, response and recovery. This forecasting capability will enable users to anticipate the consequences of future hurricanes and to develop plans and strategies for reducing risk. The methodology deals with nearly all aspects of the built environment and a wide range of different types of loss. As already discussed in literature (Hsu et al., 2011), the assessment is usually made to predict possible economic loss from different financial perspectives such as the total loss, insured loss and loss exceeding (Cova and Church, 1997). In this approach the information provided will assist state and local officials in evaluating, planning for, and mitigating the effects of expected hurricane winds and flooding with information about wind damage, disaster payments. The hurricane loss estimation methodology is based on sound scientific and engineering principals and experimental and experience data provided by HAZUS-MH project (FEMA, Lindell et al., 2006). The methodology has been tested against the judgment of experts and, to the extent possible, against records from several past hurricanes. However, uncertainties are inherent in any loss estimation methodology. They arise in part from incomplete scientific knowledge concerning hurricanes (tracks and intensities) and their effects upon buildings and facilities. They also result from the approximations and simplifications that are necessary for comprehensive analyses. For this reason, the next major hurricane to affect an area will likely be quite different than any “scenario” anticipated as part of a hurricane loss estimation study. To overcome these limitations, conducting multiple analyses and varying certain input parameters to which the losses are most sensitive should evaluate ranges of loss at the community level (Birkmann, 2005, 2006). Therefore, although the methodology offers users the opportunity to prepare comprehensive loss estimates, it should be recognized that, even with state-of-the-art techniques, uncertainties are inherent in any such estimation methodology HAZUS-MH project (FEMA, Lindell et al., 2006): first of all, uncertainties are inherent in hurricane scenario parameterization; it is only an indication of what the future may hold. This is particularly true in areas where hurricanes are poorly understood, studied, and recorded (Pompe and Rinehart, 2008; Debaine and Robin, 2012). Moreover, any region or city will have an enormous variety of buildings and facilities of different sizes, shapes and structural systems constructed over years under diverse hurricane design codes (Doukakis, 2005; Debaine and Robin, 2012). Similarly, many types of components with differing wind resistance will make up transportation and utility lifeline systems. Due to this complexity, relatively little is certain concerning the structural resistance of most buildings and other facilities. Further, there simply are not sufficient data from past phenomena or laboratory experiments to permit precise predictions of damage based on known wind forces and pressures, even for specific buildings and other structures (Moe et al., 2000; Li et al., 2000; Zhang and Grassle, 2002). The usage of the following fragility curves demands knowledge on the physical parameters of hurricane (peak gust wind speed in mph) and the structural characteristics of building components. The performance of a building class under wind loading events will be formulated probabilistically using simple concepts of structural reliability. For a single failure mode, the failure or damage probability is the probability that the wind load effect (e.g., aerodynamic pressure or impact energy) is greater than the resistance of the element. In evaluating the final results, the user has to take into account possible uncertainties in the prediction of loads and structural response HAZUS-MH project (FEMA, Lindell et al., 2006). Consequently, the users have to focus their attention on the accuracy of the model predictions, as applied to damage and loss estimates across broad classes of buildings, and on the possibility to validate results using data from past events (Frazier et al., 2010a,b; Krishnamurthy et al., 2011). The applied GIS model is potentially useful for coastal vulnerability assessment and consequence management. In the Caribbean area, the current risk prevention approach delineates hazard and defines the associated prevention measures according to the level of threat (Cutter and Emrich, 2006; Technical Report of the National Climate Change Committee, 2011; Report to the Cayman Islands' Government: Adaptation lessons learned from responding to tropical cyclones by the Cayman Islands' Government, 1988e2002, 2003). This hazard assessment is frequently conducted using a detailed modeling of well-defined centennial or historical events. In contrast, the approach applied in this study might be useful as a preliminary assessment of the potential weaknesses in the structural system. A second potential utility is its ability to generate rapid hypothetical scenarios based on pre-existing hazard models. As part of the adaptation strategy in Grand Cayman and all the other Caribbean Islands funded by the R3i projects, regional and local authorities must assess territorial vulnerability and take appropriate adaptation measures based on the sharing knowledge produced by the project. This requires the generation of multiple scenarios of possible different hazard events and assesses the relevance of different elements at risk using the vulnerability curves that the GIS approach produced (even if it could be only based on literature curves in some of the islands). Since detailed modeling is often too expensive for use in high-level scoping studies, and since uncertainties on future coastal hazards are large (e.g. Yates et al., 2011), simpler methods such as multi-criteria approaches (Le Cozannet et al., 2013) or the SPRC framework could prove to be very useful based on the results of this study. 5. Conclusion The strength of the approach proposed in this study as a problem solving method comes from the realization that similar patterns of behavior and properties appear in a variety of different A. Taramelli et al. / Ocean & Coastal Management 108 (2015) 116e130 situations. It is also capable of incorporating a variety of data sources, allowing the magnitude of such consequences can be determined in different ways depending on whether they are being considered as part of a risk screening process or as part of a more detailed risk assessment. Fundamental to the approach is the definition of the relationships between system components at a relevant scale, which leads to greater understanding and insight into the system under investigation. It can also be an iterative process and can be developed from a simple first analysis to a more detailed and comprehensive analysis. The methodology that could developed in is also intended to provide a framework within which the case study sites can collect and define information in a way which is consistent and compatible with the proposed hazard and vulnerability assessment framework. A key limitation of the GIS model is that it does not, on its own, identify the structural and economical parameters of the mapped hazard system. A quantitative representation of these parameters is just recently being developed in a GIS Decision Support System develop under Theseus (Kane et al., 2014; Zanuttigh et al., 2014) to identify critical system components still using the SPRC approach. The aim of the new implementing quantitative analysis is to provide integrated probabilistic risk assessments for rapid appraisal of risk pathways across different inputs. Based on that, some examples of mitigating structural damage to help ensure operation after disaster could include installing shutters, upgrading the roof and applying secondary water resistance. Other risk mitigation options that have to be considered are identifying, evaluating and reviewing risks, developing and managing risk awareness/reduction projects, incident investigation, arranging and coordinating appropriate property and liability insurance coverage and provision of policy advice and ministerial services to the Minister of Finance, Cabinet and Financial, Secretary on risk management issues. These other options are often used for developing an in-depth understanding of complex systems where qualitative and quantitative data and knowledge are uncertain, incomplete and/or spread across very different elements that managers have to deal with especially in emergency situations. There are many possible ways to expand this study in future works. Socio-economic and environmental vulnerability will be hopefully expanded and further implemented in R3i future funding. Acknowledgments This research was funded by the ‘R3idRegional Risk Reduction Initiative’ a project funded by the European Commission Union and implemented by UNDPdunder a contract between United Nations Development Programme (UNDP) and the Joint Venture (Joint Venture between GESP S.r.l., GIS4C B.V. and CGS-EI of UWI) for the “Provision of Services to Caribbean OCTs. Lot1: GIS and Vulnerability Assessment”. The USAID/OAS Caribbean Disaster Mitigation Project (CDMP) has supported the development of the storm hazard model TAOS/L. All data provided in this example of application are from Hazard Management Cayman Islands (2009), from Land&Survey Office of Grand Cayman and the Cayman Islands Department of Environment. References Anderson, et al., 2010. The efficacy of a programme of landslide risk reduction in areas of unplanned housing in the Eastern Caribbean. Environ. 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