In this paper, we analyze the relevance of the use of the shallow water model and the Boussinesq model to simulate tsunamis generated by a landslide. In a first part, we determine if the two models are able to reproduce waves generated by a landslide. Each model has drawbacks but it seems that it is possible to use them together to improve the simulations. In a second part we try to recover the landslide displacement from the generated wave. This problem is formulated as a minimization problem and we limit the number of parameters to determine assuming that the bottom can be well described by an empirical law.

In this work, the POD-DEIM-based parareal method introduced in [8] is implemented for solving the two-dimensional nonlinear shallow water equations using a finite volume scheme. This method is a variant of the traditional parareal method, first introduced by [22], that improves the stability and convergence for nonlinear hyperbolic problems, and uses reduced-order models constructed via the Proper Orthogonal Decomposition -Discrete Empirical Interpolation Method (POD-DEIM) applied to snapshots of the solution of the parareal iterations. We propose a modification of this parareal method for further stability and convergence improvements. It consists in enriching the snapshots set for the POD-DEIM procedure with extra snapshots whose computation does not require any additional computational cost. The performances of the classical parareal method, the POD-DEIM-based parareal method and our proposed modification are compared using numerical tests with increasing complexity. Our modified method shows a more stable behaviour and converges in fewer iterations than the other two methods.

We present a new multi-OS platform named SW2D-LEMON (SW2D for Shallow Water 2D) developed by the LEMON research team in Montpellier. SW2D-LEMON is a multi-model software focusing on shallow water-based models. It includes an unprecedented collection of upscaled (porosity) models used for shallow water equations and transportreaction processes. Porosity models are obtained by averaging the two-dimensional shallow water equations over large areas containing both a water and a solid phase. The size of a computational cell can be increased by a factor 10 to 50 compared to a 2D shallow water model, with CPU times reduced by 2 to 3 orders of magnitude. Applications include urban flood simulations as well as flows over complex topography. Besides the standard shallow water equations (the default model), several porosity models are included in the platform: (i) Single Porosity, (ii) Dual Integral Porosity, and others are currently under development such as (iii) Depth-dependent Porosity model. Various flow processes (friction, head losses, wind, momentum diffusion, precipitation/infiltration) can be included in a modular way by activating specific execution flags. We recall here the governing equations as well as numerical aspects and present the software features. Several examples are presented to illustrate the potential of SW2D.

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In this work, we implement some variations of the parareal method for speeding up the numerical resolution of the twodimensional nonlinear shallow water equations (SWE). This method aims to reduce the computational time required for a fine and expensive model, by using alongside a less accurate, but much cheaper, coarser one, which allows to parallelize in time the fine simulation. We consider here a variant of the method using reduced-order models and suitable for treating nonlinear hyperbolic problems, being able to reduce stability and convergence issues of the parareal algorithm in its original formulation. We also propose a modification of the ROM-based parareal method consisting in the enrichment of the input data for the model reduction with extra information not requiring any additional computational cost to be obtained. Numerical simulations of the 2D nonlinear SWE with increasing complexity are presented for comparing the configurations of the model reduction techniques and the performance of the parareal variants. Our proposed method presents a more stable behavior and a faster convergence towards the fine, referential solution, providing good approximations with a reduced computational cost. Therefore, it is a promising tool for accelerating the numerical simulation of problems in hydrodynamics.