GNGTS 2024 - Atti del 42° Convegno Nazionale

Session 2.1 GNGTS 2024 An improved workflow to efficiently compute local seismic probabilistic tsunami analysis (SPTHA): a case study for the harbour of Ravenna, Italy E. Baglione 1,2 , B. Brizuela 2 , M. Volpe 2 , A. Armigliato 1 , F. Zaniboni 1 , R. Tonini 2 , J. Selva 3 1 Dipartimento di Fisica e Astronomia, Università di Bologna, Italy 2 Istituto Nazionale di Geofisica e Vulcanologia, Italy 3 Università di Napoli Federico II, Napoli, Italy Introduction Tsunamis pose a significant threat to coastal communities worldwide, prompting the development of Probabilistic Tsunami Hazard Analysis (PTHA) to assess the hazard at varying Average Return Periods (ARPs), spanning from hundreds to thousands of years. By integrating data, physical and statistical models, and expert judgments, Probabilistic Tsunami Hazard Assessment (PTHA) provides a structured method for quantifying hazard and associated uncertainties (Grezio et al., 2017; Behrens et al., 2021; Davies et al., 2022). PTHA is increasingly recognized as the established best practice for effectively managing risk assessment and implementing risk mitigation measures (Løvholt et al., 2017; Tonini et al., 2020; Selva et al., 2021). Offshore PTHA studies excel in characterising hazard across a broad spectrum of earthquake-tsunami sources over extensive spatial scales while quantifying uncertainties stemming from knowledge gaps. However, their primary drawback lies in the limited modelling of tsunami shoaling and inundation, providing restricted insights into local onshore hazard. Recognizing that regional models often lack the resolution to capture specific local characteristics, the development of a local hazard model becomes imperative. A local model not only offers more accurate and detailed information but also facilitates more effective planning and mitigation strategies. Moreover, a local model proves invaluable for emergency responders and local authorities by enabling prompt and efficient evacuation and response efforts (Rafliana et al., 2022). Taking into account unique local features such as topography and coastal structures, a local model provides insights that may be overlooked in a regional model. This includes identifying vulnerable areas like small harbours or bays, which