Coastal morphodynamic emulator for early warning short-term forecasts

The use of numerical models to anticipate the effects of floods and storms in coastal regions is essential to mitigate the damages of these natural disasters. However, local studies require high spatial and temporal res-olution numerical models, limiting their use due to the involved high computational costs. This constraint be-comes even more critical when these models are used for real-time monitoring and warning systems. Therefore, the objective of this paper was to reduce the computational time of coastal morphodynamic models simulations by implementing a deep learning emulator. The emulator performance was evaluated using different scenarios run with the XBeach software, which considered different grid resolutions and the effects of a storm event in the morphodynamic patterns around a breakwater and a groin. The morphodynamic simulation time was reduced by 23%, and it was identified that the major restriction to reducing the computational cost was the hydrodynamic numerical model simulation.

Automated summary

The use of numerical models for predicting floods and storms in coastal regions is crucial for mitigating the damages caused by these natural disasters. However, the application of local studies is limited due to the high computational costs associated with the use of high spatial and temporal resolution numerical models. This paper aims to reduce the computational time of coastal morphodynamic models simulations by implementing a deep learning emulator.