Real-time, continuous, and long-term marine monitoring data benefits ocean research. This study developed a low-cost, multi-parameter, miniature wave buoy. High spatial and temporal resolution of sea surface parameters, including wind, waves, and current, can be obtained at low cost through the deployment of numerous buoys, thus forming an observation array. Tested in the laboratory water tank, the relative error of water surface slope measurement of the buoy was approximately 5.6% when the slope angle was less than 15°. For frequencies between 0.1 and 1.0 Hz, the measurement of slope spectrum was almost identical to that of the wave gauge. The buoy underestimated the slope spectrum between 1.0–1.56 Hz. A good relationship (r² = 0.75) was obtained between wind speed at 10 m above sea surface (U10) and the low-pass-filtered mean square slope (LPMSS). After incorporating the wave age into the U10 inversion process, the root mean square error (RMSE) and BIAS were reduced to 1.15 m/s and 0.02 m/s, respectively. The 2D distribution of buoy-measured slope components was used to detect the wind direction, with an RMSE of 23.7°. The spectral tail slope steepened with increasing wind speed at low wind speeds (<7 m/s). A technical flow chart of the miniature wave buoy is proposed to observe the sea surface parameters. This miniature buoy will play an essential complementary role in the growing demand for sea state monitoring, especially in nearshore oceans.

The physical processes governing coastal exchange between the surf zone, the inner shelf, and the open ocean is critical for estimating mass exchange and its impact on ecological processes. The present study combined field measurements and theoretical approaches to explore the hydrodynamics in the coastal boundary layer (CBL) in which both bottom drag and shore friction affect the transport and mixing processes. Observed drifter-cluster trajectories in a nearly alongshore-uniform coastal area showed that the occurrence of current reversal varies with cross-shore distance, which confirmed the tidal phase difference between different cross-shore distances predicted by the proposed CBL model. According to the CBL model, tidal phase difference is affected by the bottom drag coefficient and horizontal eddy viscosity coefficient. With the results of three experiments under different wave conditions, this study also discusses the effects of waves on the CBL. Data analysis based on observations indicates that the bottom drag term is closely related to the bottom shear stress induced by the interactions of waves and currents. The bottom drag coefficient under the more energetic wave condition was much greater than that under milder wave conditions during the experiment. The study also suggests that in addition to pressure gradient and bottom drag, flow structure is subject to lateral stress, which reflects the impact of shoreline roughness in the nearshore region and that the estimated eddy viscosity coefficient decreases linearly with distance from the shoreline.

Precipitation patterns are highly valued in the fields of weather forecasting, water resource management, and estuary environment research. In this study, daily and monthly precipitation TRMM data from 1998 to 2019 were selected, and EOF analysis was employed to analyze the precipitation patterns of the Taiwan Strait and its neighboring regions. We obtained the following results: (1) The rainy season (May–June) is the main contributor to precipitation in the study area. The EOF first mode reflected the overall consistency of the precipitation spatial distribution. However, within each river basin, the magnitude of precipitation variation is spatially different. The magnitude of precipitation variation is significant in the northwestern part of the Minjiang River basin, the southwestern part of the Jiulong River basin, and the southwestern corner of the Hanjiang River basin. These areas happen to correspond to the mountain areas, revealing that topographic precipitation plays a role in the spatial distribution of precipitation in the three river basins. (2) The spatial distributions of the EOF first mode and of precipitation during El Niño in the Minjiang River basin are consistent. This reveals that ENSO is probably the dominant factor in precipitation in the Minjiang River basin. The significant increase in precipitation during El Niño compared with a normal year in the Minjiang River basin confirms this point. (3) In all three strong El Niño years, 1998, 2010, and 2016, the Minjiang River basin experienced significant heavy precipitation in the fall and winter, whereas the Jiulong River and Hanjiang River basins did not (except in 2016). In other words, the Minjiang River basin is more affected by ENSO, while the Jiulong River and Hanjiang River basins are only limitedly impacted by ENSO.