Selecting model scenarios of real hydrodynamic forcings on mesotidal and macrotidal estuaries influenced by river discharges using K-means clustering. (June 2015)

• Record Type: Journal Article
• Authors: Bárcena, Javier F.; Camus, Paula; García, Andrés; Álvarez, César
• Is Part Of: Environmental modelling & software. Volume 68(2015:Jun.)
• Page Start: 70
• Page End: 82
• Publication Date: 2015-06
• Subjects: K-means (KMA) -- Tidal-river estuaries -- River flow (Q) -- Astronomical tide (A) -- Model efficiency (CE) -- Modelling scenarios -- Suances estuary (SE); Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.02.007
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: KMA has the ability to classify long-term series in small number of groups. KMA clustering approach reduces effectively the forcing dimension of the system. The method explains the variability of astronomical tides and river flows. The method selects scenarios of real forcings for modelling the mean behavior. The method helps to reduce the computational cost of estuarine numerical modelling.