The Global Earthquake Vulnerability Estimation System (GEVES): an approach for earthquake risk assessment for insurance applications

Abstract

For the insurance and reinsurance industries, earthquake loss estimation is crucial not only to adequately price its product but also to manage the accumulation risk in the face of the ever-increasing exposure in highly seismic regions. Changes in the built environment and a continuously evolving earthquake science make it a necessity for the industry to constantly refine earthquake loss estimation models. In particular, it has been recognized for a long time that the vulnerability of buildings to ground shaking is a key parameter in any earthquake risk model. Current methods tend either to rely on the limited historical damage and loss data or on the numerical simulation of the response of individual buildings to the ground-shaking produced by earthquakes. Although both methods have their advantages and pitfalls, we are proposing here a simple solution, using transparent input data, that can be realistically used for the needs of the insurance and reinsurance industry, whether detailed information about the insured structures is available or not. The resulting product is known as GEVES (Global Earthquake Vulnerability Estimation System). It is primarily intended for evaluating the mean damage ratio (MDR) suffered by a portfolio of buildings classified by use, under the action of a given earthquake scenario (i.e. an earthquake of given size at a given distance from the portfolio of buildings). A key assumption was that macroseismic intensity rather than spectral displacement would be the basis of loss estimation. The paper describes the model with emphasis on its structure and the justification for the assumptions made. In addition to a new set of earthquake vulnerability functions, the paper also provides recommendations on some aspects of the earthquake hazard, in particular about how to define macroseismic intensity at the site of interest, for a given earthquake scenario. This paper also discusses validation of the GEVES model against calculated vulnerability approaches, and the treatment of uncertainty within the model.

Quantifying Disaster Physical Damage Using Remote Sensing Data—A Technical Work Flow and Case Study of the 2014 Ludian Earthquake in China

“…Then, a posterior probability function, which is updated under the Bayesian framework, can be used to represent the calibrated results. Based on established results of the probability distribution of earthquake vulnerability, it is assumed that the prior function has the form of a beta-distribution (Spence et al 2008), and the likelihood function, which is fitted using field surveys as observational data, also follows a beta-distribution, and therefore the posterior distribution also has a beta-distribution. By taking the expected value of various posterior beta-distributions as the damage proportion of buildings with various structure types in different disaster-affected areas, a new and calibrated damage proportion matrix is obtained.…”

Review Article: A comparison of flood and earthquake vulnerability assessment indicators

“…It addresses four types of natural hazards (coastal storm surge, earthquakes, river flooding, and windstorm damage) and estimates both direct and indirect economic losses (Kircher et al, 2006;Remo and Pinter, 2012). HAZUS-MH's earthquake component uses analytically derived damage curves (Spence et al, 2008). These curves are designed for US buildings, which complicates application to different parts of the world.…”

“…ability assessment models (Spence et al, 2008). An extensive overview of earthquake loss estimation models and their respective definition of vulnerability classes has been provided by Daniell and Vervaeck (2012) and Daniell (2014).…”

Estimating building vulnerability to volcanic ash fall for insurance and other purposes

“…The majority of vulnerability functions are developed on the basis of field surveys and detailed knowledge of the building types. Guidelines for simplifying the building typology information is required in order to support the global use of vulnerability functions, as has been done for earthquake (Spence et al, 2008b), although this would best be done with much larger damage datasets than currently exist.…”