Earth Science  Human Dimensions  Natural Hazards  Tsunamis
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The NEAM Tsunami Hazard Model 2018 (NEAMTHM18) is a probabilistic hazard model for tsunamis generated by earthquakes. It covers the coastlines of the Northeastern Atlantic, the Mediterranean, and connected seas (NEAM). NEAMTHM18 was designed as a threephase project. The first two phases were dedicated to the model development and hazard calculations, following a formalized decisionmaking process based on a multipleexpert protocol. The third phase was dedicated to documentation and dissemination. The hazard assessment workflow was structured in Steps and Levels. There are four Steps: Step1) probabilistic earthquake model; Step2) tsunami generation and modeling in deep water; Step3) shoaling and inundation; Step4) hazard aggregation and uncertainty quantification. Each Step includes a different number of Levels. Level0 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 NorthEast 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 TSUMAPSNEAM project website (http://www.tsumapsneam.eu/), featuring an interactive web mapper. Although the NEAMTHM18 cannot substitute indepth 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. Appeared online in Front. Earth Sci., 05 March 2021.

Abstract: Tsunami inundation is rare on most coastlines, but large events can have devasting consequences for life and infrastructure. There is demand for inundation hazard maps to guide riskmanagement actions, such as the design of tsunami evacuation zones, tsunamiresilient infrastructure, and insurance. But the frequency of tsunamigenerating processes (e.g., large earthquakes, landslides, and volcanic collapses) is usually very uncertain. This reflects limitations in scientific knowledge, and the short duration of historical records compared to the long interevent times of dangerous tsunamis. Consequently, tsunami hazards are subject to large uncertainties which should be clearly communicated to inform riskmanagement decisions. Probabilistic Tsunami Hazard Assessment (PTHA) offers a structured approach to quantifying tsunami hazards and the associated uncertainties, while integrating data, models, and expert opinion. For earthquakegenerated tsunamis, several national and globalscale PTHAs provide databases of hypothetical scenarios, scenario occurrencerates and their uncertainties. Because these “offshore PTHAs” represent the coast at coarse spatial resolutions (~ 12 km) they are not directly suitable for onshore risk management and can only simulate tsunami waveforms accurately in deepwater, far from the coast. Yet because offshore PTHAs can use earthquake and tsunami data at global scales, they offer relatively well tested representations of earthquaketsunami sources, occurrencerates, and uncertainties. Furthermore, by combining an offshore PTHA with a highresolution coastal inundation model, the resulting onshore tsunami hazard can inprinciple be derived at spatial resolutions appropriate for risk management (~ 10 m) for any site of interest. This study considers the computational problem of rigorously transforming offshore PTHAs into sitespecific onshore PTHAs. In theory this can be done by using a highresolution hydrodynamic model to simulate inundation for every scenario in the offshore PTHA. In practice this is computationally prohibitive, because modern offshore PTHAs contain too many scenarios (on the order of 1 million) and inundation models are computationally demanding. MonteCarlo sampling offers a rigorous alternative that requires less computation, because inundation simulations are only required for a random subset of scenarios. It is also known to converge to the correct solution as the number of scenarios is increased. This study develops several approaches to reduce MonteCarlo errors at the onshore site of interest, for a given computational cost. As compared to existing MonteCarlo approaches for offshoretoonshore PTHA, the key novel idea is to use deepwater tsunami wave heights (modelled by the offshore PTHA) to estimate the relative “importance” of each scenario near the onshore site of interest, prior to inundation simulation. Scenarios are randomly sampled from the offshore PTHA in a way that overrepresents the “important” scenarios, and the theory of importance sampling enables weighting these scenarios so as to correct for the sampling bias. This can greatly reduce MonteCarlo errors for a given sampling effort. In addition, because importancesampling is analytically tractable, the variance of the MonteCarlo errors can be estimated at offshore sites prior to sampling. This helps modellers to estimate the adequacy of a proposed MonteCarlo sampling scheme prior to expensive inundation computation. The analytical variance result also enables the theory of optimalsampling to be applied in a way that to reduces the MonteCarlo variance, by nonuniformly sampling from earthquakes of different magnitudes. The new techniques are applied to an onshore earthquaketsunami PTHA in Tongatapu, the main island of Tonga. In combination the new techniques lead to efficiency improvements equivalent to simulating 418 times more scenarios, as compared with commonly used MonteCarlo methods for onshore PTHA. They also enable the hazard uncertainties in the offshore PTHA to be translated onshore, where they are of most significance to risk management decisionmaking. The greatest accuracy improvements occur for large tsunamis, and for computations that represent uncertainties in the hazard.

In November, 2018 a workshop of experts sponsored by UNESCO’s Intergovernmental Oceanographic Commission was convened in Wellington, New Zealand. The meeting was organized by Working Group (WG) 1 of the Pacific Tsunami Warning System (PTWS). The meeting brought together fourteen experts from various disciplines and four different countries (New Zealand, Australia, USA and French Polynesia) and four observers from Pacific Island countries (Tonga, Fiji), with the objective of understanding the tsunami hazard posed by the TongaKermadec trench, evaluating the current state of seismic and tsunami instrumentation in the region and assessing the level of readiness of atrisk populations. The meeting took place in the “Beehive” Annex to New Zealand’s Parliament building nearby the offices of the Ministry of Civil Defence and Emergency Management. The meeting was cochaired by Mrs. SarahJayne McCurrach (New Zealand) from the Ministry of Civil Defence and Emergency Management and Dr. Diego Arcas (USA) from NOAA’s Pacific Marine Environmental Laboratory. As one of the meeting objectives, the experts used their stateofthescience knowledge of local tectonics to identify some of the potential, worstcase seismic scenarios for the TongaKermadec trench. These scenarios were ranked as low, medium and high probability events by the same experts. While other nonseismic tsunamigenic scenarios were acknowledged, the level of uncertainty in the region, associated with the lack of instrumentation prevented the experts from identifying worse case scenarios for nonseismic sources. The present report synthesizes some of the findings of, and presents the seismic sources identified by the experts to pose the largest tsunami risk to nearby coastlines. In addition, workshop participants discussed existing gaps in scientific knowledge of local tectonics, including seismic and tsunami instrumentation of the trench and current level of tsunami readiness for atrisk populations, including realtime tsunami warnings. The results and conclusions of the meeting are presented in this report and some recommendations are summarized in the final section.

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. Appeared online in Front. Earth Sci., 29 April 2021

Offshore Probabilistic Tsunami Hazard Assessments (offshore PTHAs) provide largescale analyses of earthquaketsunami frequencies and uncertainties in the deep ocean, but do not provide highresolution onshore tsunami hazard information as required for many riskmanagement applications. To understand the implications of an onshore PTHA for the onshore hazard at any site, in principle the tsunami inundation should be simulated locally for every scenario in the offshore PTHA. In practice this is rarely feasible due to the computational expense of inundation models, and the large number of scenarios in offshore PTHAs. MonteCarlo methods offer a practical and rigorous alternative for approximating the onshore hazard, using a random subset of scenarios. The resulting MonteCarlo errors can be quantified and controlled, enabling highresolution onshore PTHAs to be implemented at a fraction of the computational cost. This study develops novel MonteCarlo sampling approaches for offshoretoonshore PTHA. Modelled offshore PTHA wave heights are used to preferentially sample scenarios that have large offshore waves near an onshore site of interest. By appropriately weighting the scenarios, the MonteCarlo errors are reduced without introducing any bias. The techniques are applied to a highresolution onshore PTHA for the island of Tongatapu in Tonga. In this region, the new approaches lead to efficiency improvements equivalent to using 418 times more random scenarios, as compared with stratifiedsampling by magnitude, which is commonly used for onshore PTHA. The greatest efficiency improvements are for rare, large tsunamis, and for calculations that represent epistemic uncertainties in the tsunami hazard. To facilitate the control of MonteCarlo errors in practical applications, this study also provides analytical techniques for estimating the errors both before and after inundation simulations are conducted. Before inundation simulation, this enables a proposed MonteCarlo sampling scheme to be checked, and potentially improved, at minimal computational cost. After inundation simulation, it enables the remaining MonteCarlo errors to be quantified at onshore sites, without additional inundation simulations. In combination these techniques enable offshore PTHAs to be rigorously transformed into onshore PTHAs, with full characterisation of epistemic uncertainties, while controlling MonteCarlo errors. Appeared online in Geophysical Journal International 11 April 2022.

At farfield coasts the largest tsunami waves often occur many hours after arrival, and hazardous waves may persist for more than a day. To simulate tsunamis at farfield coasts it is common to combine highresolution nonlinear shallow water models (covering sites of interest) with lowresolution reducedphysics globalscale models (to efficiently simulate propagation). The global propagation models often ignore friction and are mathematically energy conservative, so in theory the modelled tsunami will persist indefinitely. In contrast, real tsunamis exhibit slow dissipation at the globalscale with an energy efolding time of approximately one day. How strongly do these globalscale approximations affect nearshore tsunamis simulated at farfield coasts? To investigate this issue we compare modelled and observed tsunamis at sixteen nearshore tidegauges in Australia, which were generated by the following earthquakes: Mw 9.5 Chile 1960; Mw 9.2 Sumatra 2004; Mw 8.8 Chile 2010; Mw 9.1 Tohoku 2011; and Mw 8.3 Chile 2015. Each historic tsunami is represented with multiple earthquake source models from the literature, to prevent bias in any single source from dominating the results. The tsunami is simulated for 60 hours with a nested globaltolocal model. On the nearshore grids we solve the nonlinear shallow water equations with Manningfriction, while on the global grid we test three reducedphysics propagation models which combine the linear shallow water equations with alternative treatments of friction: 1) frictionless; 2) nonlinear Manningfriction; and 3) constant linearfriction. In comparison with data, the frictionless global model works well for simulating nearshore tsunami maxima for ~ 8 hours after tsunami arrival, and Manningfriction gives similar predictions in this period. Constant linearfriction is found to underpredict the size of early arriving waves. As the simulation duration is increased from 36 to 60 hours, the frictionless global model increasingly overestimates the observed tsunami maxima; whereas both models with globalscale friction perform relatively well. The constant linearfriction model can be improved using delayed linearfriction, where propagation is simulated with an initial frictionless period (12 hours herein). This prevents the systematic underestimation of early nearshore wave heights. While nonlinear Manningfriction offers comparably good performance, a practical advantage of the linearfriction models in this study is that their solutions can be computed, to high accuracy, with a simple transformation of frictionless solutions. This offers a pragmatic approach to improving unitsource based global tsunami simulations at late times.