PROBABILISTIC-BASED HURRICANE RISK ASSESSMENT AND ...

The "peaks-over-threshold" method involves the analysis of events that exceed a giventhreshold. The events above the given threshold can be described with the GPD(Palutikof et al. 1999). An adequately chosen threshold will result in only true peaks, andthese peaks have a Poisson arrival rate. Therefore, it is possible to model the occurrencerate of the events that exceed the threshold with a Poisson distribution (Elsner et al. 2008,Jagger and Elsner 2006, Malmstadt et al. 2010, Palutikof et al. 1999, Parisi and Lund2008).1.1.3.2 Hurricane Wind Vulnerability ModelsThe most common approach for hurricane wind damage prediction models (i.e. hurricanevulnerability models) is to develop vulnerability curves for structures. A hurricanevulnerability curve presents the most likely mean damage a structure may sustain as afunction of mean wind speed. For the development of vulnerability curves, the level ofdamage of the structure must be defined, e.g. whether looking at damage states (e.g. rooffailure) or the percentage of overall damage as a function of wind speed.Vulnerability curves for structures are often derived from post-disaster damage data (e.g.claims data or damage surveys). This method however requires a large data set. In orderto circumvent the need for large post-disaster damage data sets, probabilistic models havebeen developed to generate vulnerability curves for structures by making assumptions onthe structures and environment. The generated curves can be validated with pre-existingdata. This method can aid in generating damage curves for structures that have not yetexperienced hurricane damage, but it is limited to the engineering judgment of theindividual creating the model.For instance, Leicester et al. (1979) and Leicester (1981) produced one of the firsthurricane vulnerability models, creating vulnerability curves for various structures inAustralia based on cyclone damage surveys. Sparks et al. (1994) established therelationship between the gradient wind speed and damage ratio, by utilizing insurancedata. Mitsuta et al. (1996) utilized insurance claim information from Typhoons Mireilleand Flo to establish a relationship between wind speed and damage. Similarly,Bhinderwala (1995) used insurance data from Hurricane Andrew to develop a12