An oscillating water column (OWC) is designed for the extraction and conversion of wave energy into usable electrical power, rather than being a standalone renewable energy source. This review paper presents a comprehensive analysis of the mathematical modeling approaches employed in OWC systems, aiming to provide an in-depth understanding of the underlying principles and challenges associated with this innovative technology. A prominent classification within the realm of wave energy devices comprises OWC systems, which exhibit either fixed or floating configurations. OWC devices constitute a significant proportion of the wave energy converter prototypes currently operational offshore. Within an OWC system, a hollow structure, either permanently fixed or floating, extends below the water’s surface, creating an enclosed chamber where air is captured over the submerged inner free surface. This comprehensive study offers a thorough assessment of OWC technology in conjunction with air turbines. Additionally, the investigation delves into theoretical, computational, and experimental modeling techniques employed for analyzing OWC converters. Moreover, this review scrutinizes theoretical, computational, and experimental modeling methodologies, providing a holistic understanding of OWC converters. Ultimately, this work contributes a thorough assessment of OWC technology’s current state, accentuating its potential for efficient wave energy extraction and suggesting future research avenues.

This study integrated coastal watershed models and combined them with a risk assessment method to develop a methodology to investigate the impact resulting from coastal disasters under climate change. The mid-western coast of Taiwan suffering from land subsidence was selected as the demonstrative area for the vulnerability analysis based on the prediction of sea level rise (SLR), wave run-up, overtopping, and coastal flooding under the scenarios of the years from 2020 to 2039. Databases from tidal gauges and satellite images were used to analyze SLR using Ensemble Empirical Mode Decomposition (EEMD). Extreme wave condition and storm surge were estimated by numerical simulation using the Wind Wave Model (WWM) and the Princeton Ocean Model (POM). Coastal inundation was then simulated via the WASH123D watershed model. The risk map of study areas based on the analyses of vulnerability and disaster were established using the Analytic Hierarchy Process (AHP) technique. Predictions of sea level rise, the maximum wave condition, and storm surge under the scenarios of 2020 to 2039 are presented. The results indicate that the sea level at the mid-western coast of Taiwan will rise by an average of 5.8 cm, equivalent to a rising velocity of 2.8 mm/year. The analysis indicates that the Wuqi, Lukang, Mailiao, and Taixi townships are susceptive, low resistant and low resilient and reach the high-risk level. This assessment provides important information for creating an adaption policy for the mid-western coast of Taiwan.

Storm surge induced by severe typhoons has caused many catastrophic tragedies to coastal communities over past decades. Accurate and efficient prediction/assessment of storm surge is still an important task in order to achieve coastal disaster mitigation especially under the influence of climate change. This study revisits storm surge predictions using artificial neural networks (ANN) and effective typhoon parameters. Recent progress of storm surge modeling and some remaining unresolved issues are reviewed. In this paper, we chose the northeastern region of Taiwan as the study area, where the largest storm surge record (over 1.8 m) has been observed. To develop the ANN-based storm surge model for various lead-times (from 1 to 12 h), typhoon parameters are carefully examined and selected by analogy with the physical modeling approach. A knowledge extraction method (KEM) with backward tracking and forward exploration procedures is also proposed to analyze the roles of hidden neurons and typhoon parameters in storm surge prediction, as well as to reveal the abundant, useful information covered in the fully-trained artificial brain. Finally, the capability of ANN model for long-lead-time predictions and influences in controlling parameters are investigated. Overall, excellent agreement with observations (i.e., the coefficient of efficiency CE > 0.95 for training and CE > 0.90 for validation) is achieved in one-hour-ahead prediction. When the typhoon affects coastal waters, contributions of wind speed, central pressure deficit, and relative angle are clarified via influential hidden neurons. A general pattern of maximum storm surge under various scenarios is also obtained. Moreover, satisfactory accuracy is successfully extended to a much longer lead time (i.e., CE > 0.85 for training and CE > 0.75 for validation in 12-h-ahead prediction). Possible reasons for further accuracy improvement compared to earlier works are addressed.

The impact of climate change on the coastal zones of Taiwan not only affects the marine environment, ecology, and human communities whose economies rely heavily on marine activities, but also the sustainable development of national economics. The southwest coast is known as the area most vulnerable to climate change; therefore, this study aims to develop indicators to assess social vulnerability in this area of Taiwan using the three dimensions of susceptibility, resistance, and resilience. The modified Delphi method was used to develop nine criteria and 26 indexes in the evaluation, and the analytic hierarchy process method was employed to evaluate the weight of each indicator based on the perspectives of experts collected through questionnaire surveys. The results provide important information pertaining to the vulnerability of the most susceptive regions, the lowest-resistance areas, and the least resilient townships on the southwest coast. The most socially vulnerable areas are plotted based on the present analysis. Experts can consider the vulnerability map provided here when developing adaptation policies. It should be kept in mind that improving the capacities of resistance and resilience is more important than reducing susceptibility in Taiwan.

Ocean currents in the southern East China Sea (ECS) are complex and have long lacked systematic observational data. Observations from coastal radars in northern Taiwan, along with several drifters and satellite data, reveal a detailed ECS surface flow structure and thermal front. Drifter trajectories followed the seasonal flow field and were oscillatory and trapped due to strong tidal currents. Three drifters crossed the oceanic front off the northern coast of Taiwan following the tidal motion with a rapid change in the sea surface temperature by 2.5°C, which was recorded within a small distance of 500 m. A significant seasonal water mass exchange cycle occurred in the southern ECS. Starting in October, the Kuroshio Current intrudes onto the ECS shelf, causing the formation of a southwestward Northern Taiwan Coastal Current that mixes with the China Coastal Current to form a southward flow in the northern Taiwan Strait. Beginning in March, the ocean current off northwestern Taiwan shifts northward and then gradually flows eastward into the Kuroshio region. With satellite‐derived and model‐simulated surface currents contradicting each other over long periods of time, hourly coastal radar data have not only successfully explained the spatiotemporal variations in the sea surface temperature, salinity, and chlorophyll concentration observed by satellites but have also resolved the long‐standing disputes over the surface currents in the southern ECS.

The waters in the north of Taiwan are located at the southern end of the East China Sea (ECS), adjacent to the Taiwan Strait (TS), and the Kuroshio region. To understand the physical dynamic process of ocean currents and the temporal and spatial distribution of the ocean chlorophyll concentration in the north of Taiwan, hourly coastal ocean dynamics applications radar (CODAR) flow field data and geostationary ocean color imager (GOCI) data are analyzed here. According to data from December 2014 to May 2020, the water in the TS flows along the northern coast of Taiwan into the Kuroshio region with a velocity of 0.13 m/s in spring and summer through the ECS. In winter, the Kuroshio invades the ECS shelf, where the water flows into the TS through the ECS with a velocity of 0.08 m/s. The seasonal variation of ocean chlorophyll concentration along the northwestern coast of Taiwan is obvious, where the average chlorophyll concentration from November to January exceeds 2.0 mg/m³, and the lowest concentration in spring is 1.4 mg/m³. It is apparent that the tidal currents in the north of Taiwan flow eastward and westward during ebb and flood periods, respectively. Affected by the background currents, the flow velocity exhibits significant seasonal changes, namely, 0.43 m/s in summer and 0.27 m/s in winter during the ebb period and is 0.26 m/s in summer and 0.45 m/s in winter during the flood period. The chlorophyll concentration near the shore is also significantly affected by the tidal currents. Based on CODAR data, virtual drifter experiments, and GOCI data, this research provides novel and important knowledge of ocean current movement process in the north of Taiwan and indicates diurnal to seasonal variations in the ocean chlorophyll concentration, facilitating future research on the interaction between the TS, ECS, and Kuroshio.