Vertical Distribution Profiling of E. coli and Salinity in Tokyo Coastal Waters Following Rainfall Events Under Various Tidal Conditions

abstract

Urban estuarine environments face increasing water safety risks due to microbial contamination from combined sewer overflows (CSOs), particularly during heavy rainfall events. In megacities like Tokyo, where waterfronts are widely used for recreation, such contamination poses significant public health risks. The challenge is compounded by the variability in both intensity and spatial distribution of rainfall across the catchment, combined with complex tidal dynamics making effective water quality management difficult. To address this challenge, we conducted a series of hydrodynamic¿microbial fate simulations to examine the spatial and vertical behavior of Escherichia coli (E. coli) under different rainfall¿tide conditions. Focusing on the Sumida River estuary, rainfall data from eight drainage areas were classified into six event types using cluster analysis. Two contrasting events were selected for detailed analysis: a light rainfall (G2, 15 mm over 13 h) and an intense event (G6, 272 mm over 34 h). Vertical water quality profiling was performed along an 8.5 km transect from the Kanda¿Sumida River confluence to the Tokyo Bay Tunnel, illustrating E. coli and salinity. The results showed that the rainfall intensity and tidal phase at the event onset are critical in shaping both the magnitude and vertical distribution of microbial contamination. The intense event (G6) led to deep microbial intrusion (up to 6¿7 m) and major salinity disruption, while the lighter event (G2) showed surface-layer confinement. Salinity gradients were more strongly affected during G6, indicating freshwater intrusion. Tidal phase also influenced transport: the flood-high condition retained E. coli, whereas ebb-low tides facilitated downstream flushing. These findings highlight the influence of rainfall intensity and tidal timing on microbial distribution and support the use of vertical profiling in estuarine water quality management. They also support the development of dynamic, event-based water quality risk assessment tools. With appropriate local calibration, the modeling framework is transferable to other urban estuarine systems to support proactive and adaptive water quality management. © 2025 by the authors.