The densely populated Po Plain, a very deep sedimentary basin in northern Italy, is prone to heavy shaking during earthquakes. Seismic hazard assessment must account for local variation in wave amplification. Standard ground motion prediction equations may fail to picture the complexity of strong lateral gradients in seismic response, due to sharp structural heterogeneity. For this reason, there is an increasing demand for full waveform predictions for engineering applications. Here, we present an implementation of a hybrid broadband simulation based on the method of Mai et al. (Bull Seismol Soc Am 100(6):3338–3339, 2010), to obtain complete broadband seismograms of 0.1–10 Hz. With this method, low frequency (< 1 Hz) and high frequency (1–10 Hz) seismograms are simulated separately using a deterministic and a stochastic method, respectively. We apply the method to four events recorded within the Po basin, with magnitude ranging from Mw = 4.4 to Mw = 5.6. The low frequency (LF) simulation is performed using SPECFEM3D on a few test subsurface velocity models. The three-dimensional velocity model MAMBo (Molinari et al. in Bull Seismol Soc Am 105(2A):753–764, 2015)—consisting of a detailed structural description of the basin, based on extensive active-source data, embedded within a regional 3D crustal model—provided the best results for broadband simulations that most closely corresponded with the observations. It performed better than an ambient noise tomography model with more accurate S-wave velocities but less well defined layer topographies, emphasizing the importance of first order velocity discontinuities. The high frequency (HF) seismograms are simulated using the multiple scattering approach of Zeng et al. (J Geophys Res Solid Earth 96(B1):607–619, 1991). The scattering coefficients are obtained by performing a non linear inversion for each station to find best fitting synthetic envelopes. HF energy is then combined at ~ 1 Hz to match the amplitude and phase spectra of the LF signal. We are able to simulate full waveforms throughout the Po Plain, of which shaking duration matches observed data for stations located in the basin. Shaking amplitudes are generally overestimated in the low frequency simulation by the MAMBo velocity model. Updating the MAMBo velocity model with more accurate S-wave velocity information of the ambient noise tomography model should improve the fit in future simulations.

Probabilistic tsunami hazard analysis (PTHA) introduces potential biases in tsunami risk assessment if it assumes static coastlines. Global warming, in addition to geological and local factors, may affect sea-level rise in the next few decades. Here, we provide a method that integrates the expected sea-level rise into existing PTHA, updating regional models without further tsunami simulations. We perform the tsunami hazard analysis at the densely populated Mediterranean coasts, which are highly exposed to tsunami inundations, as reported by historical and instrumental evidence. PTHA and related epistemic uncertainties significantly change when we include the time-dependent components, such as: (1) vertical land movements along the coasts, and (2) future sea-level changes based on the expected climate scenarios described by the IPCC AR6 Report. Probability maps show that the mean probability of exceeding the 1 m and 2 m maximum inundation heights in 2070 has a general increase differentiating locally, with percent variations mainly in the range 10–30% of the updated time-dependent PTHA compared with the current PTHA.

The Mediterranean countries are densely populated regions with about 150 million people living on the coasts in 2005, reaching 200 million by 2030. The coastal population increases dramatically during the summer because of tourism increasing human exposure and the associated tsunami risk. Submarine earthquakes are considered the primary tsunamigenic sources in the Mediterranean Sea, and they are about 80% of the total events in the European historical catalog. However, other local nonseismic sources should be considered for tsunami hazards. Landslide-triggered tsunamis have only recently received the deserved attention, as they are the main potential source of nonseismic tsunami hazards. Volcanic activity may generate pyroclastic density currents from Somma-Vesuvius in the Gulf of Naples and Stromboli or underwater explosions in the Campi Flegrei caldera and Santorini representing the potential causes of tsunamis in the area with varying probabilities. On the other hand, meteotsunamis in the Mediterranean Sea have been observed in the Balearic Islands, Strait of Sicily, Maltese Islands and Adriatic Sea. Coastal variability caused by sea-level rise due to global warming, in addition to local vertical land movements, such as subsidence, constitutes another uncertain component in tsunami hazard evaluations for the Mediterranean Sea. In this chapter, a regional overview of the state of the art was presented by considering different tsunamigenic sources and including the coastline variability. The available tsunami risk assessments in the Mediterranean Sea are at the local scale with a high level of detail in a few selected cities (and only a few of them are based on a full probabilistic approach). At a regional scale, the major causes of tsunamis (earthquakes, submarine and subaerial failures, volcanic activity, and meteotsunamis) are present with different rates of occurrence. A regional selection of the exposed elements was introduced for a more comprehensive risk analysis. The selected exposed elements are not homogeneous in space and time and may constitute a lower limit for tsunami risk estimations.

Seismic urgent computing enables early assessment of an earthquake’s impact by delivering rapid simulation-based ground-shaking forecasts. This information can be used by local authorities and disaster risk managers to inform decisions about rescue and mitigation activities in the affected areas. Uncertainty quantification forurgent computing applications stands as one of the most challenging tasks. Present-day practice accounts for the uncertainty stemming from Ground Motion Models (GMMs), but neglects the uncertainty originating from the source model, which, in the first minutes after an earthquake, is only known approximately. In principle, earthquake source uncertainty can be propagated to ground motion predictions with physics-based simulations of an ensemble of earthquake scenarios capturing source variability. However, full ensemble simulation is unfeasible under emergency conditions with strict time constraints. Here we present ProbShakemap, a Python toolbox that generates multi-scenario ensembles and delivers ensemble-based forecasts for urgent source uncertainty quantification. The toolbox implements GMMs to efficiently propagate source uncertainty from the ensemble of scenarios to ground motion predictions at a set of Points of Interest (POIs), while also accounting for model uncertainty (by accommodating multiple GMMs, if available) along with their intrinsic uncertainty. ProbShakemap incorporates functionalities from two open-source toolboxes routinely implemented in seismic hazard and risk analyses: the USGS ShakeMap software and the OpenQuake-engine. ShakeMap modules are implemented to automatically select the set and weights of GMMs available for the region struck by the earthquake, whereas the OpenQuake-engine libraries are used to compute ground shaking over a set of points by randomly sampling the available GMMs. ProbShakemap provides the user with a set of tools to explore, at each POI, the predictive distribution of ground motion values encompassing source uncertainty, model uncertainty and the inherent GMMs variability. Our proposed method is quantitatively tested against the 30 October 2016 Mw 6.5 Norcia, and the 6 February 2023 Mw 7.8 Pazarcik earthquakes. We also illustrate the differences between ProbShakemap and ShakeMap output.