Exploring the impact of epistemic uncertainty on a regional probabilistic seismic risk assessment model
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Exploring the impact of epistemic uncertainty on a regional probabilistic seismic risk assessment model Petros Kalakonas1 · Vitor Silva2 · Amaryllis Mouyiannou3 · Anirudh Rao2 Received: 7 December 2019 / Accepted: 21 July 2020 © Springer Nature B.V. 2020
Abstract Probabilistic earthquake loss models are widely used in the (re)insurance industry to assess the seismic risk of portfolios of assets and to inform pricing mechanisms for (re)insurance contracts, as well as by international and national organizations with the remit to assess and reduce disaster risk. Such models include components characterizing the seismicity of the region, the ground motion intensity, the building inventory, and the vulnerability of the assets exposed to ground shaking. Each component is characterized by a large uncertainty, which can be classified as aleatory or epistemic. Modern seismic risk assessment models often neglect some sources of uncertainty, which can lead to biased loss estimates or to an underestimation of the existing uncertainty. This study focuses on exploring and quantifying the impact of a number of sources of uncertainties from each component of an earthquake loss model to the loss estimates. To this end, the residential exposure of Guatemala and Guatemala City were used as case studies. Moreover, a comparison of the predicted losses for an insured portfolio in the country between OpenQuake-engine and a vendor catastrophe platform was performed, assessing the potential application of OpenQuake in the (re)insurance industry. The findings from this study suggest that the uncertainty in the hazard component has the most significant effect on the loss estimates. Keywords Seismic risk · Epistemic uncertainty · Loss sensitivity · Insured portfolio · OpenQuake
1 Introduction Probabilistic earthquake loss models, similar to any other catastrophe model (CAT), are structured by four main components: hazard, exposure, vulnerability and a financial model. Such models are characterized by a large variability, due to the uncertainties associated with each component. These sources of uncertainty originate from the input of numerous * Petros Kalakonas [email protected] 1
University School for Advanced Studies (IUSS), Pavia, Italy
2
Global Earthquake Model Foundation (GEM), Pavia, Italy
3
Partner Re, Zurich, Switzerland
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Natural Hazards
parameters that define the seismicity, the ground motion intensity, the seismic vulnerability and the exposure characteristics of a building inventory. Inevitably, the loss estimates carry a high degree of variability, as different assumptions during the modelling process can lead to considerably different results (e.g. Silva 2018). Such uncertainties can be classified into aleatory and epistemic, depending on their nature and physical basis. Epistemic uncertainty arises from incomplete scientific knowledge and can be reduced in principle, such as through increased data or advanced scientific principles. These uncertainties can also be classified into
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