Abstract: The long-term (>30 years) simulation of 3D estuarine hydrodynamics with high-resolution meshgrids is still a challenge in numerical modeling because of the large data set of results and the computational cost requirements. Meso and macrotidal estuaries are governed by tidal action and could be influenced by river. The complexity of their behavior, suggest data mining methods may be particularly effective in selecting short-term series from a long-term series to identify the major modes of forcing variability. This study uses K-means clustering for two aims: explaining the variability of astronomical tides and river flows, and selecting scenarios of real forcings to obtain the mean behavior with a dimensional reduction. The application to the Suances estuary has highlighted the ability to classify long-term series in small number of groups. Before conducting any simulation, the proposal also determines the minimum and optimal number of groups to consider the combined effect of both forcings.

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