Variability of Volume Transport in the Malacca and Singapore Straits and its Implications for Environmental Transport

The Malacca and Singapore Straits (MSS) is one of the world’s most critical maritime corridors, supporting intensive navigation, port operations, and coastal activities. Mean ocean flow and volume transport in the MSS play a key role in environmental risk assessment, particularly for predicting the transport pathways of accidentally spilled oil or hazardous substances. The volume transport in the MSS is influenced by both local atmospheric forcing and remote drivers, including the South China Sea Throughflow (SCSTF), and exhibits pronounced variability across multiple time scales. In this study, a high-resolution dataset of ocean flow simulated by the NEMO ocean model over the Maritime Continent, forced by ORAS5 ocean reanalysis and ERA5 atmospheric data, is used to analyze volume transport variability in the MSS. The simulated volume transport in the MSS is investigated following comprehensive validation against observations in key passages, including the Luzon Strait, Taiwan Strait, and Karimata Strait. At interannual time scales, MSS volume transport shows moderate correlations with both the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). Strong seasonal variability is evident, driven by monsoon winds, with monthly climatology showing predominantly westward transport throughout the year, stronger in boreal winter and weaker in summer. Despite this climatological pattern, analysis of daily mean data reveals frequent eastward transport events during summer at sub-mesoscale time scales. These eastward transport events exhibit strong seasonality and show significant correlations with ENSO, with enhanced eastward transport occurring in summers following strong El Niño events. During these events, daily mean surface currents can reach magnitudes comparable to tidal currents in many parts of the strait. These results underscore the importance of accounting for short-term current variability when assessing pollutant transport and associated environmental impacts in the MSS.