Water residence time (WRT) is a crucial parameter for evaluating the rate of water exchange and it serves as a timescale for elucidating hydrodynamic processes, pollutant dispersion, and biogeochemical cycling in coastal waters. This study investigates the tidal-driven WRT patterns in the Bohai and Yellow Seas (collectively known as BYS) by employing a tidal model in conjunction with an adjoint WRT diagnostic model and explores the influence of tidal constituents on WRT. The findings indicate that the tidal-driven WRT in the BYS is approximately 2.11 years, exhibiting a significant spatially heterogeneous distribution. The WRT pattern shows a strong correlation with the pattern of tidal-driven Lagrangian residual currents (LRCs). Semidiurnal tides have a more pronounced effect on WRT than diurnal tides. Semidiurnal tides significantly reduce WRT across the entire BYS, while diurnal tides predominantly influence WRT in the Bohai Sea (BS). The M₂ tidal constituent is the most influential in decreasing WRT and enhancing water exchange, owing to its dominant energy contribution within the tidal system. In contrast, the S₂ tidal constituent has a minimal effect; however, its interaction with the M₂ tidal constituent plays a significant role in reducing the WRT. The K₁ and O₁ constituents exert more localized effects on WRT, particularly in the central BS, where their energy ratios relative to M₂ are relatively high. Although the amplitude of the S₂ constituent exceeds that of K₁ and O₁, its contribution to LRC—and consequently to WRT—is limited due to the overlapping tidal wave with M₂. This research contributes to a deeper understanding of the influence of tidal dynamics on long-term water transport and associated timescales, which are vital for enhancing predictions of material transport and ecosystem dynamics in tidal-dominated environments.

The phenological characteristics of the spring phytoplankton bloom in the mid- and high-latitude oceans, including its initiation, duration, and intensity, can be assessed using various diagnostic methods. However, there is currently a lack of systematic comparisons among these different methods. To elucidate the differences in spring bloom characteristics derived from different approaches and to identify suitable methods for shelf seas, this study comprehensively compares and evaluates the multiple methods for characterizing the spring bloom in the central Yellow Sea, based on satellite-derived chlorophyll-a (Chl-a) data from 2003 to 2020. The methods examined include concentration threshold (CT), cumulative concentration threshold (CCT), rate of change (RoC), and curve-fitting methods for determining bloom initiation; threshold and symmetric methods for estimating duration; and peak, mean, integral, and relative intensity index methods for assessing intensity. The results show that the bloom initiation determined by the CT method occurs earliest (average: Day of Year (DOY) 64), whereas the RoC method identifies a notably later initiation (average: DOY 100), approximately 40 days later. The CCT method yields an intermediate bloom initiation (average: DOY 70), with minimal interannual variability. Notably, curve-fitting methods often produce outliers (e.g., DOY 1) due to the fluctuations in Chl-a time series during winter. The threshold method yields a shorter bloom duration (average: 70 days), while the symmetric method results in a duration of more than 10 days longer. The four intensity assessment methods indicate that bloom intensity initially increased and subsequently decreased from 2003 to 2020, but the peak year varies depending on the method used. Overall, the CCT, symmetric, and relative index methods are more suitable for the Yellow Sea, as their computational results exhibit fewer outliers and relatively low standard deviations. The interannual variations in spring bloom characteristics assessed by different methods display distinct patterns and weak correlations, indicating that methodological choices can lead to divergent interpretations of spring bloom dynamics. Therefore, it is essential to carefully select methods based on research objectives and dataset characteristics.