Epistemic uncertainty in probabilistic estimates of seismic risk resulting from multiple hazard models

Quite frequently, catastrophes impact populated areas of the world, and hence the need for proper risk evaluations that support mitigation and management processes. Because of the uncertain nature of extreme natural hazards and lack of data, forecasts of the potential damage and losses before the event happens are needed. Catastrophe (CAT) models build on scenarios that represent all possible realizations of the hazard in terms of recurrence and intensity. Probabilistic risk models require the characterization of the hazards, the exposure model for the infrastructure, and its vulnerability. The main objective of this research is to compare loss exceedance curves, probable maximum loss curves, and average annual losses using four different available seismic hazard models for Chile. To isolate the effect of changing the hazard model in the risk results, the exposure and vulnerability information is fixed to the one available from the Global Assessment Report, GAR 15, and GAR ATLAS 2017. Imprecise probability theory, logic trees, and frequency and severity blends used by CAT modelers are the approaches applied and compared herein to propose either model blending or an interval of possible realizations. Both types of results have pros and cons. Blended results are point estimates which make them useful in a more traditional way, but their computation necessarily implies assigning weights to the models according to the modeler preferences. On the other hand, raw intervals of variability (without any knowledge of how the variable is distributed inside) are more transparent, as they simply state the bound of what is known without any preference, but their use is less understood among practitioners and could be even impractical.