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Application of the Variational Mode Decomposition (VMD) method to river tides

Gan, Min, Pan, Haidong ORCID:, Chen, Yongping and Pan, Shunqi ORCID: 2021. Application of the Variational Mode Decomposition (VMD) method to river tides. Estuarine, Coastal and Shelf Science 261 , 107570. 10.1016/j.ecss.2021.107570

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Tides in fluvial estuaries are distorted by non-stationary river discharge, which makes the analysis of estuarine water levels less accurate when using the conventional tidal analysis method. As a powerful and widely-used method for non-stationary and nonlinear time series, the application of Variational Mode Decomposition (VMD) method to non-stationary tides is nonexistent. This paper aims to illustrate and verify the suitability of the VMD method as a new tidal analysis tool for river tides. The efficiency of VMD is validated by the measurements from the Columbia River Estuary. VMD strictly divides different tidal species into different modes, and thus avoids mode mixing. Compared to VMD, Ensemble Empirical Mode Decomposition (EEMD), which is another commonly-used method, fails to completely solve the problem of mode mixing. The observed water levels at Longview station are decomposed into 12 modes via VMD. Based on the mean periods and amplitudes of each VMD mode, the 12 VMD modes sequentially correspond to the tidal species from the sub-tides (D0), diurnal tides (D1), semi-diurnal tides (D2), and up to D11 tides. The non-stationary characteristics of tides influenced by river discharge are accurately captured by VMD without mode mixing. The results also show that the EEMD and VMD modes can capture the subtidal signals better than the nonstationary tidal harmonic analysis tool (NS_TIDE). As a general method, the VMD model can also be used for other research purposes related to non-stationary tides, such as detiding.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 0272-7714
Date of First Compliant Deposit: 17 November 2021
Date of Acceptance: 30 August 2021
Last Modified: 06 Feb 2024 16:24

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