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The hydrodynamic flows in the Singapore regional waters (SRW) are the result of a complex mix of tide, seasonal and meteorological effects. The study of non-tidal effects or sea level anomalies (SLA) in this region has shown that it is possible to model some of these anomalies. The present study addresses the non-tidal barotropic water levels and currents in the Singapore region in detail. This analysis includes a multi-scale approach, and addresses amongst others hydrodynamic model grid resolution and the importance of resolving non-linear tide–surge interaction. The results show that the water level and current anomalies phenomena in a complex region like SRW can be effectively modelled using an approach combining non-tidal barotropic and multi-scale numerical modelling. A detailed investigation of the levels of non-linear tide–surge interaction is carried out by simulating SLA events in the Singapore regional waters during North-East (positive SLA) and South-West (negative SLA) monsoons based on ECMWF numerical weather forcing conditions. The results of combining both approaches suggest that the finer grid resolution improves the accuracy of water level and current anomalies simulations. Furthermore, the results also indicate that for the simulations of non-tidal barotropic flows in this area, non-linear tide–surge interaction is important and should be taken into account. Finally, the behaviour of the non-tidal barotropic flow in the region and its changes with tidal variation in the shallow region of the Singapore Strait is now much better understood. Therefore, for reliable operational forecast of sea level, the inclusion of non-linear tide–surge interaction should be required in numerical models to reproduce both tides and surges with improved accuracy in this region. [ABSTRACT FROM AUTHOR]

Abstract: Singapore Strait located between the South China Sea and Andaman Sea is driven by tides coming from both sides and the hydrodynamics in this area is complex. From the viewpoint of long term forecasting, however, models developed for this area suffer from limitations introduced by parametric uncertainty, absence of data for appropriate specification of forcing and lateral boundary conditions. For improving the model forecasts, a data assimilation technique based on ensemble Kalman filter is implemented and applied. Based on the latter, an ensemble based steady state Kalman filter is derived to address the computational limitation for daily operational forecasting. Via a twin experiment on a simulation period that includes a significant storm surge event (sea level anomaly) the skills of both data assimilation schemes are assessed and compared. [Copyright &y& Elsevier]