Submarine landslides can pose serious tsunami hazard to coastal communities. However, performing a comprehensive landslide tsunami hazard assessment for a given area is in general difficult in view of the large uncertainty associated with tsunamigenic source parameters, which are often only approximately defined, based on estimates of the landslide geometry, slide material properties, and resulting kinematics. Therefore, a Probabilistic Tsunami Hazard Analysis (PTHA) should be performed by considering a large number of cases, which is computationally demanding. Here, we present an efficient model based on solving the linear Mild-Slope Equation with a time-dependent source term representing the seafloor motion. This approach allows carrying out many computations, for a large number of landslide scenarios, in a Monte Carlo (MC) approach framework, at a reduced computational cost compared to other available methods, while still providing physically accurate simulations of most landslide tsunami generation and propagation processes. To further speed-up the MC simulations, a database of elementary solutions is first developed, for many landslide sources of unit amplitude motion over a small seafloor area within the possible landslide footprint. For each unit source, the resulting tsunami elevations are computed and saved at many locations of interest. In the MC simulations, a large number of landslide scenarios are defined by randomly selecting slide parameters within their statistical distributions and each is then simulated for their specific bottom motion using a linear combination of the pre-computed unit sources. Hence, each resulting tsunami is quickly computed at the locations of interest by linear superposition. The paper presents the model validation against two tests cases and describes its novel methodology to perform multiple landslide tsunami scenarios.
Iorio, V., Bellotti, G., Cecioni, C., & Grilli, S.T. (2021). A numerical model for the efficient simulation of multiple landslide-induced tsunamis scenarios. OCEAN MODELLING, 168, 101899 [10.1016/j.ocemod.2021.101899].