Author(s): Farzin Samsami; Zhi Yung Tay; Peng Shu Ng; Zhi Jing Feng; Elisa Y. M. Ang; Peng Cheng Wang

Linked Author(s):

Keywords: Singapore Waters; Sea Level Anomalies; Harmonic Analysis; Tidal Constituents; Hydrodynamic Modeling; Delft3D

Abstract: Sea level anomalies and rises in lowland regions like Singapore are a significant concern for coastal communities and management. Singapore, an island city off the Malay Peninsula, has a dynamic marine environment vulnerable to rising sea levels. Due to the complexity of oceanic and atmospheric processes, many factors may affect sea level variations in Singapore's waters, including tides, wave climate, atmospheric conditions, and human activities. Scrutinizing the sea level anomalies in this area is crucial to reducing the risks of climate change and rising sea levels. To effectively investigate sea level anomalies, it is necessary to consider comprehensively the different tidal constituents that capture the predominant tidal signals in this area. Due to the complexity of tidal patterns, which can be significantly affected by both seasonal cycles and the interaction of different tidal constituents, this paper examines the significance of tidal constituents in understanding sea level anomalies in Singapore waters. This study performed sea level and harmonic analysis on the measured water level time series at Tanjong Pagar tide station in the Singapore Strait for 37 years (1984–2020). Our findings reveal significant differences in the contribution of different tidal constituent combinations, highlighting the importance of integrating multiple tidal constituents to accurately predict and interpret sea level fluctuations and anomalies. A Delft3D model was developed for the Singapore region to simulate the complex tidal dynamics and interactions within the surrounding waters. This model incorporates various factors such as bathymetry and hydrodynamics to provide a comprehensive understanding of the tidal dynamics in the region. This research output will also be used to develop a pure data-driven machine-learning algorithm for storm surge predictions in Singapore.

DOI: https://doi.org/10.64697/978-90-835589-7-4_41WC-P1662-cd

Year: 2025