Return period and Pareto analyses of 45 years of tropical cyclone data (1970-2014) in the Philippines

Return period and Pareto analyses of 45 years of tropical cyclone data (1970-2014) in the Philippines

The epiphenomena of tropical cyclones (TCs) such as landslides, storm surges, and floods cause the largest loss of life and property in the Philippines. In order to improve the disaster risk management efforts of the country, it is necessary to evaluate the return periods (RPs) or chance of occurrence (CoC) of TCs. Hence, this study generally aimed to investigate the relationship of the RPs/CoC, sea surface temperature (SST) anomaly associated with the Interdecadal Pacific Oscillation (IPO), and the cost of socio-economic damages and the number of deaths caused by TCs. The Weibull parametric models and the Pareto principle were utilised to achieve this overarching objective. Using the maximum sustained wind speed (v), forty-five (45) years of TC data (1970–2014) within the Philippines Area of Responsibility (PAR) were analysed by applying the stationary and non-stationary stochastic modelling techniques. The stationary Weibull probability density function revealed that TCs Rita (1978), Dot (1985) and Haiyan (2013) occupy the wind speed region of 61≥v≤64m/s with a probability of 0.4% for any given year. On the other hand, the analysis of the cumulative distribution function revealed a 60% probability of TCs for the cumulative years with a maximum sustained wind speed of at most 38 m/s. This indicates the central estimate of the wind speed from 1970 to 2014 with TCs Ruby (1988) and Vicki (1998) as the observed cases. Furthermore, the probability values on the annual CoC maps depict the indicative positions of TCs, either singly, co-shared or cross-shared, that made landfall (or not) in the Philippines. Results from the non-stationary stochastic modelling revealed that the low probability values on the decadal CoC maps indicate the locations where extreme TC events are likely to occur within PAR; hence, showing the areas in the country that are more at-risk. The relationship of SST anomaly and CoC values disclosed that the TCs are intensified in the northern Philippines and south of West Philippines Sea during the positive(+) phase and the negative(-) phase of the IPO, respectively. Finally, the Pareto analysis revealed that 80% of the TC-related damage cost and the number of deaths are shared by three (3) different stationary and non-stationary RPs with TCs Ike (1984), Nina (1987), Fengshen (2008), Mike (1990), Parma (2009), and Haiyan (2013) as the observed extreme events. In the absence of accurate or updated cyclone risk models, the communities that are highly vulnerable to TCs can use the stationary and non-stationary stochastic CoC models as an early warning tool for disaster preparedness. Ultimately, the results of this study can provide significant insights to support the Philippines in their pursuit of improving cyclone resilience programs.

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