Journal of Sea Research最新文献

To analyze the coupling relationship in marine industry agglomeration and ecological environment system, this study used the formula of location entropy coefficient for measuring the development agglomeration status of marine industry. Meanwhile, it also separately measures the degree of agglomeration of the primary, secondary, and tertiary industries in the ocean to reveal their degree of agglomeration in the region. In addition, a coupled correlation analysis model was designed. It uses grey relational analysis to reveal the relationships between data series in situations of insufficient data. Finally, a coupled coordination evaluation model was proposed. It evaluates the coupling status between the marine industry cluster and the ecological environment by calculating the capacity coupling coefficient, coupling degree, and coupling co scheduling. The results show that from 2013 to 2022, the agglomeration trend of the marine industry in Jiaodong Peninsula Province has undergone different changes. The location entropy coefficient of the primary industry increased from 1.3047 to 1.0987, while the location entropy coefficient of the secondary industry gradually increased from 0.2486 to 1.1141. The coupling coordination degree R between the marine industry agglomeration system and the ecological environment system has increased from 0.3986 to 0.6253. From 2018 to 2022, the coupling between the marine industry and ecological environment development has increased, gradually moving towards stability and coordination. This indicates that over the past decade, the development of the marine industry in Jiaodong Peninsula Province has placed greater emphasis on ecological protection, and has formed a positive interaction, promoting the coordinated development.

Tidal power generation, being one of the earliest and largest developed new energy technologies, has evolved into a mature and increasingly competitive source of energy. This article delves into the evaluation of tidal energy's socioeconomic impact, establishing a connection between its environmental benefits and regional economic development. The focus is on marine economic efficiency, and the super-efficiency DEA model is employed to gauge this efficiency. The study conducts a thorough analysis of the green ocean economic efficiency in three major regions, introducing the Malmquist efficiency index for a comprehensive understanding of changing trends. Utilizing an economic effect model, the research explores the contribution of tidal energy development to regional economic growth, delving into industry-related effects. Through these analyses, we aim to provide a clearer and more comprehensive insight into the positive influence of tidal energy on regional socioeconomics. This understanding is pivotal for guiding sustainable tidal energy development and formulating relevant policies.

Coastal cities, despite being close to expansive oceans, are still susceptible to water constraints. The water resource tax reform pilot has been instrumental in promoting water conservation and effective utilization, so contributing significantly to the green growth of the coastal cities where it has been implemented. Utilizing data from prefecture-level cities in China's coastal areas from 2007 to 2019 and adopting the Green Total Factor Productivity as a measure of green growth, this study is dedicated to exploring the impact of water resources tax reform on the green growth of coastal cities. It treats the water resource tax reform pilot as a quasi-natural experiment and utilizes multi-period DID and PSM-DID methodologies to examine the effects of this reform on Green Total Factor Productivity. The findings indicate that the water resource tax reform pilot has made a significant contribution to green growth, primarily by reducing water consumption and enhancing governmental focus on marine environmental regulation. Expanding upon these findings, the article provides suggestions in several domains like the establishment of distinct tax rates, allocation of tax money, government oversight, and implementation of taxes on agricultural water resources. These findings can serve as a scientific benchmark for the development and execution of water resource tax policies in other areas.

To investigate the relationship between the marine industry and regional development, and assess its impact on the marine economy. The study creates an evaluation index system for the coupling relationship between the marine economy and regional development. It applies coupling theory and combines the hierarchical analysis method with the entropy weight method to determine the weight of each index. By constructing the coupling degree and coupling coordination degree model, this paper quantitatively analyzes the coupling relationship and applies the coefficient of variation to assess the coordinated development differences between different regions. The study utilized data from the official China Marine Statistical Yearbook. The analysis revealed an upward trend in the coupling degree of the marine economy and regional development during the study period, increasing from 0.58 to 0.72. This indicated a significant strengthening of the interaction and synergistic development between the two. In typical coastal areas, the coupling degree increased from 0.62 to 0.78, indicating the significant contribution of the marine industry to the region's comprehensive economic development. The study proposes policy recommendations to enhance the adjustment of the marine industrial structure and technological innovation, promote inter-regional cooperation and resource sharing, and strengthen marine environmental protection and sustainable resource utilization. These measures aim to promote the development of the marine economy while safeguarding marine resources.

The Earth's oceans serve as vast heat storage reservoirs and the ocean currents, forming a heat transfer zone, act as a buffer for climate change. The oceans play a crucial role in the water cycle, carbon cycle, and nitrogen cycle, interacting with the atmosphere. However, the increasing occurrence of heatwaves in recent years has disrupted the stability of the oceans and affected their function as a global thermostat. This paper examines relevant literature from the Web of Science database spanning from 1996 to 2023. Our analysis reveals the evolving research hotspots regarding the impact of heatwaves on ocean carbon sinks, progressing from “carbon dioxide - carbon emissions - ocean - seawater - organic matter - marine sediment” over the past decades. Keyword frequency and centrality analysis indicate a deepening focus in this field of study. The research areas primarily revolve around climate change, carbon dioxide, ocean variability, the influence of high temperatures on the carbon cycle, the weakening of the ocean carbon sink function, and ocean temperature. This study summarizes research findings on the mechanisms by which high temperature heatwaves affect ocean carbon sinks, alterations in ocean carbon cycle patterns, their impacts on marine ecosystems, and factors influencing seawater temperature and heatwave occurrence. Finally, the paper discusses novel findings in this field and proposes countermeasures to enhance ocean sink capacity amidst the warming trend.

In light of the issues associated with marine organisms adhering to hull surfaces during sea transportation, leading to additional fuel consumption and hull damage, this paper proposes a novel design for an underwater hull cleaning robot. Building upon existing technology, the robot integrates traditional mechanical cleaning and cavitation jet cleaning methods, enhancing both approaches. It can first mechanically clean most of the attachments of the hull, and then deeply clean the remaining residues of the cavitation jet, so as to achieve efficient and non-destructive comprehensive cleaning of the hull surface, thereby reducing the ocean transportation cost. We used SolidWorks to conduct modeling and solid simulation experiments, and fluent to conduct fluid simulation experiments. The experimental results show that the robot can meet the cleaning needs of offshore platforms.

Argo data is multidimensional observational data from ocean floats, which has long been plagued by data anomalies. Currently, the anomaly detection problem in Argo data still faces many challenges, as anomalies may involve complex relationships between multiple variables, leading to suboptimal performance with traditional machine learning methods. Thanks to the advancement of deep learning, it has become the predominant methodology for anomaly detection, demonstrating notable performance. The Transformer model has shown significant potential in the field of data anomaly detection, with its core Self-Attention mechanism capable of learning relationships between variables. We introduce Fast Fourier Transform (FFT) into the Transformer model, enabling the model to better capture periodic patterns and complex relationships in multivariate data, learning normal data patterns to improve the method for Argo data anomaly detection. Through experiments conducted on three public datasets and the Argo dataset, the enhanced model outperforms the original model in terms of performance. This also demonstrates the potential of FFT in multidimensional data anomaly detection, providing new insights into addressing anomaly detection challenges in real-world complex datasets.

The detachment of the cyclonic eddy (CE) from the mainstream on the north side of the Agulhas Return Current (ARC) is first observed and analyzed with a multiparameter eddy dataset and eddy detection technique. It is found that there exists a special form (ACA) of two anticyclonic eddies (AE) sandwiching a cyclonic eddy (CE). The cyclonic eddy is then locally shed from the jet and shifts to the west before finally being recaptured by the mainstream. From observations, cyclonic eddy detachments in ACA form can be classified into two categories based on the detachment mode and on the sea surface height (SSH) data. Using reanalysis data, satellite observation data and Argo data, the analysis reveals that the positional relationship between the anticyclonic eddies and cyclonic eddy affects the detachment type. In addition, the existence of the ARC itself, with its giant meandering morphology and dissipative effect, also provides conditions for the detachment of the cyclonic eddy.

The lack of observational data in the northern part of the Bay of Bengal (BoB) makes it challenging to investigate the upper oceanic responses to tropical cyclones. To overcome this challenge, a high-resolution Regional Ocean Model (ROMS) is set up with horizontal resolution of 0.03° × 0.03° and 50 vertical layers for the northern BoB, with the daily output of the First Institute of Oceanography surface wave-tide-circulation coupled ocean model (FIO-COM) as the boundary condition, and hourly ERA5 data as atmospheric forcings. This regional model is systematically validated and then utilized to reconstruct the upper ocean response due to the super cyclonic storm Amphan over the BoB from May 16 to 20, 2020. The upper ocean responses to Amphan in the northern BoB is well reconstructed with this regional model. On the right side of the cyclone track, sea surface temperature (SST) cooling (4 °C) and increased sea surface salinity (0.5 psu) are well reproduced. The primary oceanic triggering forces intensified the cyclone through the extraordinarily high SST (>31 °C) and deep isothermal layer depth. Tropical cyclone heat potential was high (over 100Kj cm⁻²) during the early stages of the cyclone, which aided the transformation of a depression into a super cyclonic storm. Vertical entrainment and horizontal advection had a major influence in the pronounced cooling within the mixed layer.

Finer resolution is one of the development trends in ocean surface waves simulation and forecasting. However, high-resolution numerical models for ocean surface waves have led to an enormous increase in computational complexity, posing a challenge with respect to balancing computational efficiency and timeliness. To meet the demand for refined ocean surface waves simulation/forecasting and to address the computational efficiency challenge of high-resolution ocean surface waves models, we propose a downscaling model called the Global location-Specific transformation Downscaling Network (GSDNet) based on the non-autoregressive fusion network (NAFNet). By incorporating global location-specific transformation and introducing a land–sea distribution indicator, GSDNet can quickly and accurately map low-resolution significant wave heights to high-resolution grids. The results show that, compared with traditional interpolation methods such as the bilinear, inverse distance weight interpolation (IDW), and bicubic methods, the GSDNet model can reduce the global mean absolute error (MAE) by >77%. Compared with those of FourCastNet (FCN), the Koopman neural operator (KNO), the original NAFNet, and residual networks in deep learning from empirical downscaling methods (DL4DS_ResNet), the MAE decreases by >21%. Furthermore, the GSDNet model outperforms the other downscaling methods at the coastal boundary and for identifying the maximum significant wave height. In this work, we provide an effective solution for balancing computational efficiency and timeliness, which is important for improving the accuracy and reliability of ocean surface waves simulation/forecasting.