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Probabilistic seismic hazard analysis (PSHA), as a tool to assess the probability that ground motion of a given intensity or larger is experienced at a given site and time span, has historically comprised the basis of both building design codes in earthquake-prone regions and seismic risk models. The PSHA traditionally refers solely to mainshock events and typically employs a homogeneous Poisson process to model their occurrence. Nevertheless, recent disasters, such as the 2010–2011 Christchurch sequence or the 2016 Central Italy earthquakes, to name a few, have highlighted the potential pitfalls of neglecting the occurrence of foreshocks, aftershocks, and other triggered events, and pinpointed the need to revisit the current practice. Herein, we employ the epidemic-type aftershock sequence (ETAS) model to describe seismicity in Central Italy, investigate the model’s capability to reproduce salient features of observed seismicity, and compare ETAS-derived one-year hazard estimates with ones obtained with a standard mainshock-only Poisson-based hazard model. A companion paper uses the hazard models derived herein to compare and contrast loss estimates for the residential exposure of Umbria in Central Italy.

In current practice, most earthquake risk models adopt a “declustered” view of seismicity, that is, they disregard foreshock, aftershock, and triggered earthquakes and model seismicity as a series of independent mainshock events, whose occurrence (typically) conforms to a Poisson process. This practice is certainly disputable but has been justified by the false notion that earthquakes of smaller magnitude than the mainshock cannot induce further damage than what was caused by the latter. A companion paper makes use of the epidemic-type aftershock sequence (ETAS) model fitted to Central Italy seismicity data to describe the full earthquake occurrence process, including “dependent” earthquakes. Herein, loss estimates for the region of Umbria in Central Italy are derived using stochastic event catalogs generated by means of the ETAS model and damage-dependent fragility functions to track damage accumulation. The results are then compared with estimates obtained with a conventional Poisson-based model. The potential gains of utilizing a model capable of capturing the spatiotemporal clustering features of seismicity are illustrated along with a discussion on the various details and challenges of such considerations.