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High-resolution wave forecasts are of critical importance for coastal safety, hazard prevention, and energy transition. Examples that benefit from detailed information of coastal sea states range from the adaptation of coastal protection to beach goer and marine safety and even include the growing market of marine renewable energy. While regional and global wave forecasts exist, local high-resolution details are often not accessible due to the associated computational cost.In our work we use a neural network to apply a so-called super-resolution approach that can turn wave forecasts computed over coarse grids into substantially higher resolution at very low computation cost and with only little loss of overall quality. The idea is to train the neural network with pairs of coarse and fine-grid computations, so that later on wave computations over coarse grids can be converted into a higher resolution. While the amount of saved computation time varies with the level of resolution between coarse and fine grid, the super-resolution approach can be more than 50-times faster than a direct high-resolution computation and still provide good accuracy.Here we will present a case study on a 44-year hindcast of the French Basque coast, with which we trained and tested multiple networks. We will comment on the dependency of model performance on the amount of training data and the difficulties of an unstructured grid, compared to a structured one. More specifically, structured grids can be handled as regular images, which makes the adaptation of classical and powerful convolutional neural networks to the problem relatively easy. The application to irregular grids, however, is not trivial and requires different approaches like graph neural networks or multi-layer perceptrons. Lastly, we will compare the performance of a surrogate model, that is, a neural network that simply replaces completely a spectral wave model like SWAN, with our super-resolution approach, that uses coarse wave model computations as an input. We argue that both approaches are complementary and have their advantages and disadvantages in specific settings.

In the literature, OpenFOAM has been used to simulate landslide tsunamis, modeling the landslide as a solid or a two-phase flow. Here we present an approach using three phases (air, water and sediment) modeled as Newtonian fluids. This 3D model is validated against two benchmarks with deformable landslides: one subaerial (Viroulet et al. 2016) and one submarine (Grilli et al. 2017). These benchmarks are also run by a 2D depth-integrated model, AVALANCHE, recently used to reproduce the 2017 Karrat Fjord, Greenland and the 2018 Anak Krakatau, Indonesia events. Both models are able to reproduce either the water waves or the landslide behavior but not both at the same time.Considering OpenFOAM as a reference code, sensitivity studies on the slope angle, the landslide viscosity and the landslide initial submergence showed that AVALANCHE produces similar results for slope angles between 10 and 45°, for subaerial or close to the surface landslide and/or for low viscosity values. In the other cases (submarine landslides and higher viscosity values) results indicated that OpenFOAM should be preferred to a 2D depth-integrated model. References:Grilli, S., Shelby, M. & Kimmoun, O. (2017), ‘Modeling coastal tsunami hazard from submarine mass failures: effect of slide rheology, experimental validation, and case studies off the US East Coast’, Natural Hazards 86, 353-391.Viroulet, S., Sauret, A., Kimmoun, O. & Kharif, C. (2016), Tsunami waves generated by cliff collapse: comparison between experiments and triphasic simulations, in E. Pelinovsky & C. Kharif, eds, ‘Extreme Ocean Waves’, Springer International Publishing, Cham, pp. 173-190.

In this paper, we present a new source assessment of the La Palma collapse scenario previously described and studied in Abadie et al. (2012). Three scenarios (i.e., slide volumes of 20, 40 and 80 km3) are considered, from the initiation of the slide to the water waves generation, using THETIS, a 3D Navier–Stokes model. The slide is considered as a Newtonian fluid whose viscosity is adjusted to approximate a granular behavior. After 5 minutes of propagation with THETIS, the generated water wave is transferred into FUNWAVE-TVD for 15 minutes of Boussinesq model simulation. Then, four different depth-averaged codes are used to propagate the wave to the Guadeloupe area, Europe and French coasts. Finally, the wave impact in terms of run-up is evaluated through direct computations in specific areas or using theoretical formulas. Although the wave source appears reduced due to the rheology used compared to former works, the wave impact is still significant for the maximum slide volume considered on surrounding islands and coasts, as well as on remote most exposed coasts such as Guadeloupe. In Europe and in France, the wave impact is moderate (for specific areas in Spain and Portugal) to weak (Atlantic French coast). The comparison between the different wave models in overlapping computational regions shows an overall agreement in terms of first wave amplitude and time of arrival, but differences appear in the trailing waves.