Probabilistic Tsunami Hazard and Risk Analysis: A Review of Research Gaps

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.

Probabilistic Tsunami Hazard Analysis of Inundated Buildings Following a Subaqueous Volcanic Explosion Based on the 1716 Tsunami Scenario in Taal Lake, Philippines

A probabilistic hazard analysis of a tsunami generated by a subaqueous volcanic explosion was performed for Taal Lake in the Philippines. The Taal volcano at Taal Lake is an active volcano on Luzon Island in the Philippines, and its eruption would potentially generate tsunamis in the lake. This study aimed to analyze a probabilistic tsunami hazard of inundated buildings for tsunami mitigation in future scenarios. To determine the probabilistic tsunami hazard, different explosion diameters were used to generate tsunamis of different magnitudes in the TUNAMI-N2 model. The initial water level in the tsunami model was estimated based on the explosion energy. The tsunami-induced inundation from the TUNAMI-N2 model was overlaid on the distribution of buildings. The tsunami hazard analysis of inundated buildings was performed by using the maximum inundation depth in each explosion case. These products were used to calculate the probability of the inundated building given the occurrence of a subaqueous explosion. The results from this study can be used for future tsunami mitigation if a tsunami is generated by a subaqueous volcanic explosion.

Probabilistic Tsunami Hazard Analysis: High Performance Computing for Massive Scale Inundation Simulations

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.

Probabilistic tsunami hazard analysis for Tuzla test site using Monte Carlo simulations

In this study, time-dependent probabilistic tsunami hazard analysis (PTHA) is performed for Tuzla, Istanbul, in the Sea of Marmara, Turkey, using various earthquake scenarios of Prince Island Fault (PIF) within the next 50 and 100 years. The Monte Carlo (MC) simulation technique is used to generate a synthetic earthquake catalogue, which includes earthquakes having moment magnitudes between Mw6.5 and 7.1. This interval defines the minimum and maximum magnitudes for the fault in the case of an entire fault rupture, which depends on the characteristic fault model. Based on this catalogue, probability of occurrence and associated tsunami wave heights are calculated for each event. The study associates the probabilistic approach with tsunami numerical modeling. The tsunami numerical code NAMI DANCE was used for tsunami simulations. According to the results of the analysis, distribution of probability of occurrence corresponding to tsunami hydrodynamic parameters is represented. Maximum positive and negative wave amplitudes show that tsunami wave heights up to 1 m have 65 % probability of exceedance for the next 50 years and this value increases by 85 % in the Tuzla region for the next 100 years. Inundation depth also exceeds 1 m in the region with probabilities of occurrence of 60 % and 80 % for the next 50 and 100 years, respectively. Moreover, probabilistic inundation maps are generated to investigate inundated zones and the amount of water penetrated inland. Probability of exceedance of 0.3 m wave height ranges between 10 % and 75 % according to these probabilistic inundation maps, and the maximum inundation distance calculated in the entire earthquake catalogue is 60 m in this test site. Furthermore, synthetic gauge points are selected along the western coast of Istanbul by including Tuzla coasts. Tuzla is one of the areas that shows high probability exceedance of 0.3 m wave height, which is around 90 %, for the next 50 years while this probability reaches up to more than 95 % for the next 100 years.

On the landslide tsunami uncertainty and hazard

Landslides are the second most frequent tsunami source worldwide. However, their complex and diverse nature of origin combined with their infrequent event records make prognostic modelling challenging. In this paper, we present a probabilistic framework for analysing uncertainties emerging from the landslide source process. This probabilistic framework employs event trees and is used to conduct tsunami uncertainty analysis as well as probabilistic tsunami hazard analysis (PTHA). An example study is presented for the Lyngen fjord in Norway. This application uses a mix of empirical landslide data combined with expert judgement to come up with probability maps for tsunami inundation. Based on this study, it is concluded that the present landslide tsunami hazard analysis is largely driven by epistemic uncertainties. These epistemic uncertainties can be incorporated in the probabilistic framework. Conducting a literature analysis, we further show examples of how landslide and tsunami data can be used to better constrain landslide uncertainties, combined with statistical and numerical analysis methods. We discuss how these methods, combined with the probabilistic framework, can be used to improve landslide tsunami hazard analysis in the future.

Probabilistic Tsunami Hazard Analysis: High Performance Computing for Massive Scale Monte Carlo type Inundation Simulations

<p>Probabilistic Tsunami Hazard Analysis (PTHA) is an approach to quantifying the likelihood of exceeding a specified metric of tsunami inundation at a given location within a given time interval. It provides scientific guidance for decision making regarding coastal engineering and evacuation planning. PTHA requires a discretization of many potential tsunami source scenarios and an evaluation of the probability of each scenario. The classical approach of PTHA has been the quantification of the tsunami hazard offshore, while estimates of the inundation at a given coastal site have been limited to a few scenarios. PTHA, with an adequate discretization of source scenarios, combined with high-resolution 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. 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 made a regional and local long-term hazard assessment feasible. PTHA is one of the so-called Pilot Demonstrators of the EC-funded ChEESE project (Center of Excellence for Exascale Computing in the Solid Earth) where a workflow has been developed with three main stages: source specification and discretization, efficient numerical inundation simulation for each scenario using the HySEA numerical tsunami propagation model, and hazard aggregation. HySEA calculates tsunami offshore propagation and inundation using a system of telescopic topo-bathymetric grids. In this presentation, we illustrate the workflows of the PTHA as implemented for HPC applications, including preliminary simulations carried out on intermediate scale GPU clusters. Finally, we delineate how planned upscaling to exascale applications can significantly increase the accuracy of local tsunami hazard analysis.</p><p>This work is partially funded by the European Union’s Horizon 2020 Research and Innovation Program under grant agreement No 823844 (ChEESE Center of Excellence, www.cheese-coe.eu).</p>