ALBERT

Description: Finite-fault models for the 2010 Mw 8.8 Maule, Chile earthquake indicate bilateral rupture with large-slip patches located north and south of the epicenter. Previous studies also show that this event features significant slip in the shallow part of the megathrust, which is revealed through correction of the forward tsunami modeling scheme used in tsunami inversions. The presence of shallow slip is consistent with the coseismic seafloor deformation measured off the Maule region adjacent to the trench and confirms that tsunami observations are particularly important for constraining far-offshore slip. Here, we benchmark the method of Optimal Time Alignment (OTA) of the tsunami waveforms in the joint inversion of tsunami (DART and tide-gauges) and geodetic (GPS, InSAR, land-leveling) observations for this event. We test the application of OTA to the tsunami Green’s functions used in a previous inversion. Through a suite of synthetic tests we show that if the bias in the forward model is comprised only of delays in the tsunami signals, the OTA can correct them precisely, independently of the sensors (DART or coastal tide-gauges) and, to the first-order, of the bathymetric model used. The same suite of experiments is repeated for the real case of the 2010 Maule earthquake where, despite the results of the synthetic tests, DARTs are shown to outperform tide-gauges. This gives an indication of the relative weights to be assigned when jointly inverting the two types of data. Moreover, we show that using OTA is preferable to subjectively correcting possible time mismatch of the tsunami waveforms. The results for the source model of the Maule earthquake show that using just the first-order modeling correction introduced by OTA confirms the bilateral rupture pattern around the epicenter, and, most importantly, shifts the inferred northern patch of slip to a shallower position consistent with the slip models obtained by applying more complex physics-based corrections to the tsunami waveforms. This is confirmed by a slip model refined by inverting geodetic and tsunami data complemented with a denser distribution of GPS data nearby the source area. The models obtained with the OTA method are finally benchmarked against the observed seafloor deformation off the Maule region. We find that all of the models using the OTA well predict this offshore coseismic deformation, thus overall, this benchmarking of the OTA method can be considered successful.

Description: Tsunamis are unpredictable and infrequent but potentially large impact natural disasters. To prepare, mitigate and prevent losses from tsunamis, probabilistic hazard and risk analysis methods have been developed and have proved useful. However, large gaps and uncertainties still exist and many steps in the assessment methods lack information, theoretical foundation, or commonly accepted methods. Moreover, applied methods have very different levels of maturity, from already advanced probabilistic tsunami hazard analysis for earthquake sources, to less mature probabilistic risk analysis. In this review we give an overview of the current state of probabilistic tsunami hazard and risk analysis. Identifying research gaps, we offer suggestions for future research directions. An extensive literature list allows for branching into diverse aspects of this scientific approach.

Description: The NEAM Tsunami Hazard Model 2018 (NEAMTHM18) is a probabilistic hazard model for tsunamis generated by earthquakes. It covers the coastlines of the North-eastern Atlantic, the Mediterranean, and connected seas (NEAM). NEAMTHM18 was designed as a three-phase project. The first two phases were dedicated to the model development and hazard calculations, following a formalized decision-making process based on a multiple-expert protocol. The third phase was dedicated to documentation and dissemination. The hazard assessment workflow was structured in Steps and Levels. There are four Steps: Step-1) probabilistic earthquake model; Step-2) tsunami generation and modeling in deep water; Step-3) shoaling and inundation; Step-4) hazard aggregation and uncertainty quantification. Each Step includes a different number of Levels. Level-0 always describes the input data; the other Levels describe the intermediate results needed to proceed from one Step to another. Alternative datasets and models were considered in the implementation. The epistemic hazard uncertainty was quantified through an ensemble modeling technique accounting for alternative models’ weights and yielding a distribution of hazard curves represented by the mean and various percentiles. Hazard curves were calculated at 2,343 Points of Interest (POI) distributed at an average spacing of ∼20 km. Precalculated probability maps for five maximum inundation heights (MIH) and hazard intensity maps for five average return periods (ARP) were produced from hazard curves. In the entire NEAM Region, MIHs of several meters are rare but not impossible. Considering a 2% probability of exceedance in 50 years (ARP≈2,475 years), the POIs with MIH 〉5 m are fewer than 1% and are all in the Mediterranean on Libya, Egypt, Cyprus, and Greece coasts. In the North-East Atlantic, POIs with MIH 〉3 m are on the coasts of Mauritania and Gulf of Cadiz. Overall, 30% of the POIs have MIH 〉1 m. NEAMTHM18 results and documentation are available through the TSUMAPS-NEAM project website (http://www.tsumaps-neam.eu/), featuring an interactive web mapper. Although the NEAMTHM18 cannot substitute in-depth analyses at local scales, it represents the first action to start local and more detailed hazard and risk assessments and contributes to designing evacuation maps for tsunami early warning.

Description: Tsunami warning centres face the challenging task of rapidly forecasting tsunami threat immediately after an earthquake, when there is high uncertainty due to data deficiency. Here we introduce Probabilistic Tsunami Forecasting (PTF) for tsunami early warning. PTF explicitly treats data- and forecast-uncertainties, enabling alert level definitions according to any predefined level of conservatism, which is connected to the average balance of missed-vs-false-alarms. Impact forecasts and resulting recommendations become progressively less uncertain as new data become available. Here we report an implementation for near-source early warning and test it systematically by hindcasting the great 2010 M8.8 Maule (Chile) and the well-studied 2003 M6.8 Zemmouri-Boumerdes (Algeria) tsunamis, as well as all the Mediterranean earthquakes that triggered alert messages at the Italian Tsunami Warning Centre since its inception in 2015, demonstrating forecasting accuracy over a wide range of magnitudes and earthquake types.

Description: The complexity of coseismic slip distributions influences the tsunami hazard posed by local and, to a certain extent, distant tsunami sources. Large slip concentrated in shallow patches was observed in recent tsunamigenic earthquakes, possibly due to dynamic amplification near the free surface, variable frictional conditions or other factors. We propose a method for incorporating enhanced shallow slip for subduction earthquakes while preventing systematic slip excess at shallow depths over one or more seismic cycles. The method uses the classic k−2 stochastic slip distributions, augmented by shallow slip amplification. It is necessary for deep events with lower slip to occur more often than shallow ones with amplified slip to balance the long-term cumulative slip. We evaluate the impact of this approach on tsunami hazard in the central and eastern Mediterranean Sea adopting a realistic 3D geometry for three subduction zones, by using it to model ~ 150,000 earthquakes with Mw from 6.0 to 9.0. We combine earthquake rates, depth-dependent slip distributions, tsunami modeling, and epistemic uncertainty through an ensemble modeling technique. We found that the mean hazard curves obtained with our method show enhanced probabilities for larger inundation heights as compared to the curves derived from depth-independent slip distributions. Our approach is completely general and can be applied to any subduction zone in the world.

Description: Probabilistic Tsunami Hazard Analysis (PTHA) quantifies the probability of exceeding a specified inundation intensity at a given location within a given time interval. PTHA provides scientific guidance for tsunami risk analysis and risk management, including coastal planning and early warning. Explicit computation of site-specific PTHA, with an adequate discretization of source scenarios combined with high-resolution numerical inundation modelling, has been out of reach with existing models and computing capabilities, with tens to hundreds of thousands of moderately intensive numerical simulations being required for exhaustive uncertainty quantification. In recent years, more efficient GPU-based High-Performance Computing (HPC) facilities, together with efficient GPU-optimized shallow water type models for simulating tsunami inundation, have now made local long-term hazard assessment feasible. A workflow has been developed with three main stages: 1) Site-specific source selection and discretization, 2) Efficient numerical inundation simulation for each scenario using the GPU-based Tsunami-HySEA numerical tsunami propagation and inundation model using a system of nested topo-bathymetric grids, and 3) Hazard aggregation. We apply this site-specific PTHA workflow here to Catania, Sicily, for tsunamigenic earthquake sources in the Mediterranean. We illustrate the workflows of the PTHA as implemented for High-Performance Computing applications, including preliminary simulations carried out on intermediate scale GPU clusters. We show how the local hazard analysis conducted here produces a more fine-grained assessment than is possible with a regional assessment. However, the new local PTHA indicates somewhat lower probabilities of exceedance for higher maximum inundation heights than the available regional PTHA. The local hazard analysis takes into account small-scale tsunami inundation features and non-linearity which the regional-scale assessment does not incorporate. However, the deterministic inundation simulations neglect some uncertainties stemming from the simplified source treatment and tsunami modelling that are embedded in the regional stochastic approach to inundation height estimation. Further research is needed to quantify the uncertainty associated with numerical inundation modelling and to properly propagate it onto the hazard results, to fully exploit the potential of site-specific hazard assessment based on massive simulations.

Description: A methodology for a comprehensive probabilistic tsunami hazard analysis is presented for the major sources of tsunamis (seismic events, landslides, and volcanic activity) and preliminarily applied in the Gulf of Naples (Italy). The methodology uses both a modular procedure to evaluate the tsunami hazard and a Bayesian analysis to include the historical information of the past tsunami events. In the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0001 the submarine earthquakes and the submarine mass failures are initially identified in a gridded domain and defined by a set of parameters, producing the sea floor deformations and the corresponding initial tsunami waves. Differently volcanic tsunamis generate sea surface waves caused by pyroclastic density currents from Somma‐Vesuvius. In the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0002 the tsunami waves are simulated and propagated in the deep sea by a numerical model that solves the shallow water equations. In the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0003 the tsunami wave heights are estimated at the coast using the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0004's amplification law. The selected tsunami intensity is the wave height. In the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0005 the probabilistic tsunami analysis computes the long‐term comprehensive Bayesian probabilistic tsunami hazard analysis. In the prior analysis the probabilities from the scenarios in which the tsunami parameter overcomes the selected threshold levels are combined with the spatial, temporal, and frequency‐size probabilities of occurrence of the tsunamigenic sources. The urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0006 probability density functions are integrated with the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0007 derived from the historical information based on past tsunami data. The urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0008 probability density functions are evaluated to produce the hazard curves in selected sites of the Gulf of Naples.