National-level prediction of expected seismic loss based on historical catalogue

Abstract

The frequency and severity of natural catastrophes have increased significantly over the last few years, with countries around the world having to face huge economic and human losses. Italy in particular is very seismic-prone, being located in the precise area of convergence between the African and Eurasian lithospheric plates. In addition, most Italian cities are densely populated and have many old and historical buildings, making the country even more exposed and vulnerable in terms of potential losses. Recently, new regulations gave householders the chance to buy insurance against earthquakes. Unfortunately, the widespread risk perception in Italy is very low among the population. In addition, insurance premiums can be extremely high given the low-probability-high-risk of cash flow and insolvency problems potentially incurred by an insurance company if there is a high magnitude earthquake. The aim of the methodology proposed in this paper is to give insurers an engineering instrument with which to quantify expected losses in the case of an earthquake. This will enable insurance companies to model innovative and more affordable financial products by optimizing the quantification of premiums. Seismic events with a magnitude greater than 4 from 217 a.C. to 2012 have been selected from the historical catalogue of the National Institute of Geophysics and Volcanology, and statistical simulations of earthquake scenarios are performed for each of them. In particular, the peak ground acceleration is simulated, based on the ground motion prediction equation of Bindi et al. (Bull Earthq Eng 7(3):591–608, 2009). Actual exposure is assumed for the Italian building stock, which is modelled according to the database of the National Institute of Statistics. Finally, in order to compute the total losses for the entire national building stock, the annual expected losses are quantified according to the procedure demonstrated in Asprone et al. (Struct Saf 44:70–79, 2013).