Comprehensive Analysis of Trends in Ocean Surface Current by Delineating Kuroshio Extent Using an Optimized Segmentation Algorithm

Most studies investigated Kuroshio on quantitative values measured at standard grid locations or along a transect. However, this research delineates Kuroshio from its surrounding waters, and surface current trends are analyzed from 1993 to 2020, extending from a single location to a basin scale. This unique approach combines segmentation and image processing. When integrated with the goodness of variance fit greater than 0.8, it proves more efficacious than conventional threshold/histogram techniques in delineating patchy Kuroshio extent contributing to oceanography and climate studies. The generalized trend from delineated Kuroshio exhibited a systemwide weakening. To analyze whether the trend is uniform or varies with Latitude, delineated Kuroshio is divided into five sections based on the hydrological characteristics. The trend was analyzed using three statistical, two frequency, and one Bayesian approach on a regional mean and a pixel-by-pixel basis. Weakening in Luzon and along Taiwan is worth noting. In contrast, a strengthening in the Tokara Strait to Nagoya and Nagoya-150°E. Interestingly, the transition zone from weakening to strengthening in the Kuroshio South-II. Regarding computational efficiency, Detecting Breakpoints and Estimating Segments in Trend (DBEST) and Wavelet Transform (WT) outperformed the other methods. Ensemble Empirical Mode Decomposition (EEMD) and Seasonal Trend Loess (STL) have similar levels of efficiency. Breaks for Additive Season and Trend (BFAST) is designed for a linear trend, while EEMD is suitable for a general trend. The Bayesian Estimator of Abrupt, Seasonal Change and Trend (BEAST) combines multiple models to reduce overfitting and produces highly correlated trends from GlobCurrent and drifter data.