Search

Regional and global tsunami hazard analysis requires simplified and efficient methods for estimating the tsunami inundation height and its related uncertainty. One such approach is the amplification factor (AF) method. Amplification factors describe the relation between offshore wave height and the maximum inundation height, as predicted by linearized plane wave models employed for incident waves with different wave characteristics. In this study, a new amplification factor method is developed that takes into account the offshore bathymetry proximal to the coastal site. The present AFs cover the North-Eastern Atlantic and Mediterranean (NEAM) region. The model is the first general approximate model that quantifies inundation height uncertainty. Uncertainty quantification is carried out by analyzing the inundation height variability in more than 500 high-resolution inundation simulations at six different coastal sites. The inundation simulations are undertaken with different earthquake sources in order to produce different wave period and polarity. We show that the probability density of the maximum inundation height can be modeled with a log-normal distribution, whose median is quite well predicted by the AF. It is further demonstrated that the associated maximum inundation height uncertainties are significant and must be accounted for in tsunami hazard analysis. The application to the recently developed TSUMAPS-NEAM probabilistic tsunami hazard analysis (PTHA) is presented as a use case.

Subduction systems globally terminate, allowing plate-motion to be transferred from the oceanic megathrust onto continental and/or oceanic transform faults. The mechanism of this kinematic transition over earthquake timescales is, however, poorly understood due to a lack of relevant data. Here, we study the 2016 Mw 7.8 Kaikōura Earthquake in New Zealand, the first large instrumentally-recorded earthquake across a subduction-termination, to investigate this transfer mechanism in detail. We find that the Kaikōura Earthquake, unlike standard subduction earthquakes globally, involved a predominance (∼80%) of coseismic-slip on upper-plate faults and minor triggered-slip on the underlying oceanic subduction-thrust. In the months following the earthquake, the subduction-thrust accommodated most of the earthquake's afterslip down-dip of its co-seismic rupture zone. This top-down strain-release mechanism is in accord with local geological, geodetic and historical seismicity data which suggest that the bulk of plate-convergence (>75%) is accommodated in the upper-plate. We suggest, therefore, that this alternative strain-release mechanism, which is distinct from standard plate-boundary earthquakes, is characteristic of subduction-terminations and results in the majority of seismic/tsunami hazard being on steep near-surface faults.

Applying probabilistic methods to infrequent but devastating natural events is intrinsically challenging. For tsunami analyses, a suite of geophysical assessments should be in principle evaluated because of the different causes generating tsunamis (earthquakes, landslides, volcanic activity, meteorological events, and asteroid impacts) with varying mean recurrence rates. Probabilistic Tsunami Hazard Analyses (PTHAs) are conducted in different areas of the world at global, regional, and local scales with the aim of understanding tsunami hazard to inform tsunami risk reduction activities. PTHAs enhance knowledge of the potential tsunamigenic threat by estimating the probability of exceeding specific levels of tsunami intensity metrics (e.g., run-up or maximum inundation heights) within a certain period of time (exposure time) at given locations (target sites); these estimates can be summarized in hazard maps or hazard curves. This discussion presents a broad overview of PTHA, including (i) sources and mechanisms of tsunami generation, emphasizing the variety and complexity of the tsunami sources and their generation mechanisms, (ii) developments in modeling the propagation and impact of tsunami waves, and (iii) statistical procedures for tsunami hazard estimates that include the associated epistemic and aleatoric uncertainties. Key elements in understanding the potential tsunami hazard are discussed, in light of the rapid development of PTHA methods during the last decade and the globally distributed applications, including the importance of considering multiple sources, their relative intensities, probabilities of occurrence, and uncertainties in an integrated and consistent probabilistic framework.

SUMMARY The various uncertainties in the earthquake-triggered tsunami threat assessment are difficult to quantify and/or integrate into the tsunami early warning process. Uncertainties in the (seismic) input parameters and the lack of knowledge about the earthquake slip distribution contribute most to the total uncertainty in real-time evaluated tsunami assessment. We present a method how to integrate and quantify these uncertainties in the warning process by evaluating a tsunami warning level probability distribution with a Bayesian network (BN) approach. As soon as an earthquake is detected, the seismic source parameter estimates are evaluated and a probabilistic overview on different tsunami warning levels is provided, feasible to support a decision maker at a warning center with important additional data. A BN system has been developed exemplarily for the region Sumatra. In this paper, we describe the method of BN generation by ancestral sampling, we critically analyse the assumptions made and weight the pro and cons of the BN approach. A case study demonstrates the workflow of the BN system in real-time and reveals the promising power of a BN analysis in the framework of early warning.

We present the GITEWS approach to source modeling for the tsunami early warning in Indonesia. Near-field tsunami implies special requirements to both warning time and details of source characterization. To meet these requirements, we employ geophysical and geological information to predefine a maximum number of rupture parameters. We discretize the tsunamigenic Sunda plate interface into an ordered grid of patches (150×25) and employ the concept of Green's functions for forward and inverse rupture modeling. Rupture Generator, a forward modeling tool, additionally employs different scaling laws and slip shape functions to construct physically reasonable source models using basic seismic information only (magnitude and epicenter location). GITEWS runs a library of semi- and fully-synthetic scenarios to be extensively employed by system testing as well as by warning center personnel teaching and training. Near real-time GPS observations are a very valuable complement to the local tsunami warning system. Their inversion provides quick (within a few minutes on an event) estimation of the earthquake magnitude, rupture position and, in case of sufficient station coverage, details of slip distribution.

The Indonesian archipelago is known for the occurrence of catastrophic earthquake-generated tsunamis along the Sunda Arc. The tsunami hazard associated with submarine landslides however has not been fully addressed. In this paper, we compile the known tsunamigenic events where landslide involvement is certain and summarize the properties of published landslides that were identified with geophysical methods. We depict novel mass movements, found in newly available bathymetry, and determine their key parameters. Using numerical modeling, we compute possible tsunami scenarios. Furthermore, we propose a way of identifying landslide tsunamis using an array of few buoys with bottom pressure units.

Global Positioning System (GPS) has been proved to be a powerful tool for measuring co-seismic ground displacements with an application to seismic source inversion. Whereas most of the tsunamis are triggered by large earthquakes, GPS can contribute to the tsunami early warning system (TEWS) by helping to obtain tsunami source parameters in near real-time. Toward the end of 2012, the second phase of the BeiDou Navigation Satellite System (BDS) constellation was accomplished, and BDS has been providing regional positioning service since then. Numerical results indicate that precision of BDS nowadays is equivalent to that of the GPS. Compared with a single Global Satellite Navigation System (GNSS), combined BDS/GPS real-time processing can improve accuracy and especially reliability of retrieved co-seismic displacements. In the present study, we investigate the potential of BDS to serve for the early warning system of tsunamis in the South China Sea region. To facilitate early warnings of tsunamis and forecasting capabilities in this region, we propose to distribute an array of BDS-stations along the Luzon Island (Philippines). By simulating an earthquake with Mw = 8 at the Manila trench as an example, we demonstrate that such an array will be able to detect earthquake parameters in real time with a high degree of accuracy and, hence, contribute to the fast and reliable tsunami early warning system in this region.

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., Probabilistic tsunami forecasting (PTF) defines an approach to tsunami early warning based on uncertainty quantification, enhancing forecast accuracy and enabling rational decision making. PTF is here developed for near-source tsunami warning, and tested in hindcasting mode over a wide range of past earthquakes.