Search Results (144)

This study investigates the spatiotemporal dynamics of zooplankton communities in the tropical low-latitude Northwestern Pacific Ocean based on field surveys conducted in August 2021 and November 2022. Redundancy analysis identified nitrate, silicate, temperature, and salinity as the primary factors influencing community structure. The distribution of dominant zooplankton groups exhibited close correlations with key environmental gradients, showing distinct habitat preferences corresponding to different hydrographic conditions. Zooplankton abundance in August 2021 was significantly higher than that in November 2022, which is presumably attributed to eddy-induced nutrient upwelling and enhanced primary productivity. Comparisons with adjacent marine regions reveal general consistency in overall zooplankton abundance and community species composition, while the observed seasonal discrepancies are closely associated with local unique hydrographic characteristics. These results highlight the role of nutrient–temperature–salinity interactions in structuring zooplankton communities and underscore their sensitivity to environmental variability. The findings provide a scientific basis for understanding pelagic ecosystem dynamics in oligotrophic waters and for developing management strategies under changing climate and oceanographic conditions. Full article

The resolution and accuracy of airborne/spaceborne SAR are continuously improving, making it an effective means for observing ocean dynamic processes and detecting marine targets. In contrast, utilizing its unique orbital angular momentum (OAM) mode, vortex radar does not require temporal accumulation to achieve azimuthal resolution, making it particularly suitable for observing moving sea surfaces. This capability enables stable and continuous monitoring of dynamic ocean scenes. This paper proposes a vortex radar imaging method based on three-dimensional sea surface scattering characteristics: first, a three-dimensional wind-driven sea surface geometric model is established based on the Elfouhaily sea spectrum, and its scattering characteristics under different incident angles, wind speeds, and wind directions are analyzed using the semi-deterministic facet-based two-scale method; then, two-dimensional range-azimuth imaging is achieved through coordinate transformation, echo modeling, pulse compression, and fast Fourier transform (FFT) in OAM mode domain, with the correctness of the imaging algorithm verified through multiple point target imaging results. Finally, simulation results of two-dimensional sea surface vortex imaging under different incident angles are presented, and the influence of wind speed and direction on sea surface vortex imaging is analyzed. The study shows that the vortex imaging system can effectively reflect wave fluctuations and wind direction characteristics, demonstrating the feasibility and potential of vortex radar imaging in oceanographic applications. Full article

Lanternfishes (Myctophidae) dominate mesopelagic ecosystems and play a central role in pelagic food webs through their high biomass and diel vertical migration, yet detailed information on their age structure and growth dynamics remains limited in the Northwest Pacific Ocean. This study reconstructs age, growth patterns, and life-history strategies of D. gigas and D. perspicillatus using sagittal otolith microstructure analysis. Specimens were collected during oceanographic surveys conducted in 2023 and 2024, and individual ages were estimated by counting daily otolith growth increments. Somatic growth trajectories were evaluated using multiple nonlinear growth models, including the von Bertalanffy, Gompertz, and Logistic functions, and growth dynamics were further assessed through derivative-based growth speed analyses. The results reveal pronounced interspecific differences in growth strategy and longevity. D. perspicillatus exhibited rapid early somatic growth, a compressed age structure, and an early approach to asymptotic length, indicating a short-lived life-history strategy characterized by early growth deceleration and high population turnover. In contrast, D. gigas showed faster early growth, prolonged somatic development, greater inter-individual variability, and substantially larger maximum body size, reflecting delayed maturation and extended lifespan. Otolith microstructural zonation clearly corresponded to larval, juvenile, and adult growth phases in both species. The predominance of younger age classes in the catch and interannual differences in size structure were primarily attributed to ontogenetic habitat shifts, cohort composition, and sampling availability rather than intrinsic changes in growth dynamics. Full article

The residual current is the ocean current after the tidal component has been removed. Understanding the spatiotemporal distribution characteristics of sea surface residual currents is key to revealing the local current field evolution and typical physical oceanographic processes. The Taiwan Strait is in the East Asian monsoon region, where residual currents are significantly influenced by monsoons during periods of high wind speeds. However, the characteristics and dynamic mechanisms of residual currents under low wind speed conditions (≤5 m/s) remain unclear. Based on high-frequency surface wave radar current data and wind field reanalysis data, this study analyzed the characteristics of residual currents in the southwestern Taiwan Strait under low wind speed conditions, focusing on two orthogonal directions: cross-shore and along-shore. During these periods, residual currents exhibit counter-wind current characteristics. These currents cross the Taiwan Bank and generate wave signals with wavelengths ranging from 35.6 km to 65.8 km and durations of 6 to 12 h in the Xiapeng Depression area. These fluctuations are triggered by the combined timing of low winds and nonlinear current–topography interactions. In terms of dynamic mechanisms, the Coriolis force term and the acceleration term dominate the momentum equations in both two orthogonal directions, indicating that the current field is in a non-steady inertial adjustment phase during this period. Furthermore, this study constructs a two-layer ocean model of rotationally modified gravity waves to analyze the influences of topography, oceanic stratification, and steady current velocity on the characteristics of residual current fluctuations under low wind speed conditions. The theoretical model yields spatial scales that closely match the observed wavelength characteristics. Full article

Modern oceanographic studies demonstrate that marginal seas and semi-restricted marine environments, including epicontinental seas and carbonate platforms, are highly sensitive to changes in circulation, freshwater input, stratification, and redox conditions, allowing climatic perturbations to be recorded with high fidelity. Understanding the behavior of such systems under icehouse conditions is therefore important for interpreting climate variability in both ancient and modern oceans. The Late Paleozoic Ice Age was a prolonged icehouse interval characterized by repeated glacial and interglacial oscillations, yet its climate dynamics are still mainly constrained by Gondwanan glacigenic records and low-latitude carbonate successions. High-resolution climate information from mid-latitude regions remains limited. The purpose of this study is to obtain high-resolution mid-latitude geochemical constraints on climate variability during the Late Paleozoic Ice Age using a semi-restricted marine carbonate succession. Specifically, this study aims to (1) establish high-resolution carbon and oxygen isotope records from well-preserved carbonate samples spanning the Visean to Asselian interval; (2) identify and characterize major glacial to interglacial cycles recorded in the succession; (3) evaluate the extent to which semi-restricted paleogeography amplifies isotopic responses relative to coeval low-latitude open-marine settings and (4) assess the climatic significance of a short-lived negative carbon isotope excursion during the middle Bashkirian. Here we present high-resolution carbon and oxygen isotope records from a Visean to Asselian marine carbonate succession deposited in a semi-restricted basin. Stable isotope analyses of well-preserved carbonate samples document temporal variations in carbonate carbon and oxygen isotopes. The records resolve at least three major glacial to interglacial cycles, with isotope shifts substantially larger than those reported from coeval low-latitude open-marine settings. Carbon isotope variations reach up to 7.7‰, while oxygen isotope variations reach up to 9.2‰. These pronounced responses are attributed to semi-restricted paleogeography, facies heterogeneity, and the sensitivity of marine carbonate systems to stratification, redox variability, and organic carbon cycling. A short-lived negative carbon isotope excursion during the middle Bashkirian may record a Northern Hemisphere deglaciation event superimposed on the broader Gondwanan icehouse background, a signal that is not clearly expressed in other regions. Overall, this study describes new mid-latitude geochemical constraints on Late Paleozoic climate variability and offers valuable analogs for understanding climate responses in modern marginal marine systems. Full article

Maritime safety is a key element of sustainable maritime transportation, particularly in strait regions with dense vessel traffic and dynamic environmental conditions that increase collision risk. Based on historical records, ship collisions can result in severe human casualties, environmental pollution, cargo and infrastructure damage, operational disruptions, and substantial economic losses; therefore, a reliable and integrated safety assessment is essential to support safe, efficient, and sustainable maritime transportation. This study proposes a novel safety index framework to assess the ship’s collision risk by integrating vessel characteristics, ship encounter conditions, operational time parameters, and oceanographic factors such as currents and waves. The analysis is based on questionnaire data, AIS records, and oceanographic information collected over a one-month period with a three-minute temporal resolution. Case studies are conducted in the Bali Strait and the Lombok Strait using grid-based spatial segmentation to represent spatial risk patterns. Two safety index models are developed. Model I emphasizes vessel, encounter, and temporal factors, while Model II extends the assessment by fully integrating oceanographic conditions. To improve interpretability and practical applicability, the calculated safety index is further transformed into a normalized safety index with values bounded between 0 and 1, allowing for explicit risk classification. A multivariate contribution analysis is applied to identify dominant risk factors. The results show that the maritime risk in both straits is mainly influenced by vessel traffic intensity, sailing hours, days of the week, and environmental conditions. High-risk zones in the Bali Strait are concentrated near Ketapang and Gilimanuk Ports, while elevated risks in the Lombok Strait are observed near Padangbai and Lembar Ports and along the ALKI II shipping route. Full article

An ongoing challenge in pelagic oceanography and limnology is to quantify and understand the distribution of suspended particles and particle aggregates with sufficient temporal and spatial fidelity to understand their dynamics. These particles include biotic (mesoplankton, organic fragments, fecal pellets, etc.) and abiotic (dusts, precipitates, sediments and flocks, anthropogenic materials, etc.) matter and their aggregates (i.e., marine snow), which form a large part of the total particulate matter > 200 μm in size in the ocean. The transport of organic material from surface waters to the deep-sea floor is of particular interest, as it is recognized as a key factor controlling the global carbon cycle and hence, a critical process influencing the sequestration of carbon dioxide from the atmosphere. Here we describe the development of an oceanographic instrument, the Pelagic Laser Tomographer (PLT), that uses high-resolution optical technology, coupled with post-processing analysis, to scan the 3D content of the water column to detect and quantify 3D distributions of small particles. Existing optical instruments typically trade sampling volume for spatial resolution or require large, complex platforms. The PLT addresses this gap by combining high-resolution laser-sheet imaging with large effective sampling volumes in a compact, deployable system. The PLT can generate spatial distributions of small particles (~100 µm and larger) across large water volumes (order 100–1000 m³) during a typical deployment, and allow measurements of particle patchiness over spatial scales to less than 1 mm. The instrument’s small size (6 kg), high resolution (~100 µm in each 3000 cm² tomographic image slice), and analysis software provide a tool for pelagic studies that have typically been limited by high cost, data storage, resolution, and mechanical constraints, all usually necessitating bulky instrumentation and infrequent deployment, typically requiring a large research vessel. Full article

Submarine power cables for offshore wind farms experience continuous cyclic loading from environmental forces and floating-platform motions, making fatigue performance a critical design factor. This study combined global and local analyses to investigate the fatigue behavior of a 66 kV dry-type submarine cable installed using a flexible pull-in installation system. A global dynamic analysis using site-specific meteorological and oceanographic data provided time-series displacement responses that were used to evaluate the fatigue damage to the metallic components of the cable. The results indicated that the minimum fatigue life of 8.71 × 10⁴ cycles occurred at the upper metallic sheath near the fixed end, with a corresponding cumulative damage of 1.147 × 10⁻⁵. Fatigue accumulation was predominantly governed by lateral (y-direction) displacement, while axial and vertical displacement components contributed minimally. Furthermore, the predicted fatigue life of the metallic sheath varied by a factor of up to 3.6 depending on the selected curve, comparing the cyclic stress amplitude and number of cycles to failure (S–N curve), highlighting the importance of accurate material fatigue data. These findings emphasize the need for careful evaluation of the environmental loading and sheath fatigue properties in flexible pull-in installation system-based submarine cable system designs. Full article

Recent advances in satellite observations have expanded the use of Sea Surface Temperature (SST) and Sea Surface Height (SSH) data in climate and oceanography, yet their low spatial resolution limits fine-scale analyses. We propose HiT_DS, a modular hierarchical Transformer framework for high-resolution downscaling of SST and SSH fields. To address challenges in multiscale feature representation and physical consistency, HiT_DS integrates three key modules: (1) Enhanced Dual Feature Extraction (E-DFE), which employs depth-wise separable convolutions to improve local feature modeling efficiently; (2) Gradient-Aware Attention (GA), which emphasizes dynamically important high-gradient structures such as oceanic fronts; and (3) Physics-Informed Loss Functions, which promote physical realism and dynamical consistency in the reconstructed fields. Experiments across two dynamically distinct oceanic regions demonstrate that HiT_DS achieves improved reconstruction accuracy and enhanced physical fidelity, with selective module combinations tailored to regional dynamical conditions. This framework provides an effective and extensible approach for oceanographic data downscaling. Full article

Global container shipping faces increasing pressure to reduce fuel consumption and greenhouse gas (GHG) emissions while still meeting strict port schedules under highly uncertain terminal operations and met-ocean conditions. However, most existing voyage-planning approaches either ignore real port operation variability or treat fuel optimization and just-in-time (JIT) arrival as separate problems, limiting their applicability in actual operations. This study presents a data-driven just-in-time voyage optimization framework that integrates port-side uncertainty and marine environmental dynamics into the routing process. A dwell-time prediction model based on Gradient Boosting was developed using port throughput and meteorological–oceanographic variables, achieving a validation accuracy of R² = 0.84 and providing a data-driven required time of arrival (RTA) estimate. A Transformer encoder model was constructed to forecast fuel consumption from multivariate navigation and environmental data, and the model achieved a segment-level predictive performance with an R² value of approximately 0.99. These predictive modules were embedded into a Deep Q-Network (DQN) routing model capable of optimizing headings and speed profiles under spatially varying ocean conditions. Experiments were conducted on three container-carrier routes in which the historical AIS trajectories served as operational benchmark routes. Compared with these AIS-based baselines, the optimized routes reduced fuel consumption and CO₂ emissions by approximately 26% to 69%, while driving the JIT arrival deviation close to zero. The proposed framework provides a unified approach that links port operations, fuel dynamics, and ocean-aware route planning, offering practical benefits for smart and autonomous ship navigation. Full article

The Digital Twins of the Ocean (DTOs) represent an emerging framework for monitoring, simulating, and predicting ocean dynamics, supporting a range of applications relevant to understanding and responding to the global climate system. By integrating large-scale, multi-sourced datasets with advanced numerical models, DTOs provide a powerful tool for climate science. This review examines the role of machine learning (ML) in advancing DTOs applications, addressing the limitations of traditional methodologies under current conditions of increasing data availability from satellites, in situ sensors, and high-resolution numerical models. We highlight how ML serves as a versatile tool for enhancing DTOs capabilities, including real-time forecasting, correcting model biases, and filling data gaps where conventional approaches fall short. Furthermore, we review surrogate models that aim to complement or replace traditional physical models, offering increasing accuracy and the appeal of much faster inference for forecasts, and the insertion of hybrid models, which couple physics-based simulations with ML algorithms and are proving to be continuously improving in accuracy for complex oceanographic tasks as bigger datasets become available and methodologies evolve. This paper provides a comprehensive review of ML applications within DTOs, focusing on key areas such as water quality and marine biodiversity, ports, marine pollution, fisheries, and renewable energy. The review concludes with a discussion of future research directions and the potential of ML to foster more robust and practical DTOs, ultimately supporting informed decision-making for sustainable ocean management. Full article

With the growing diversity and complexity of marine monitoring requirements, the scientific deployment of moored buoys has attracted increasing attention. To address the limitations of traditional methods—such as inconsistent knowledge representation, insufficient logical reasoning capacity, and poor adaptability to dynamic marine environments—this study proposes an adaptive optimization method for moored buoy site selection integrating ontology reasoning and numerical computation. The proposed approach constructs an ontology model covering key concepts such as buoy specifications, monitoring objectives, and deployment requirements, and further defines formalized reasoning rules to enable automated judgment of deployment feasibility, sensor configuration, and spatial conflict resolution for moored buoy siting. Based on this semantic framework, a spatio-temporal comprehensive variation index (STCVI) is established by integrating temperature, salinity, and current velocity to characterize dynamic oceanographic conditions. Furthermore, a coverage-first greedy algorithm is designed to determine buoy deployment locations, enabling dynamic optimization and environmental adaptability of the buoy station layout. To verify the feasibility and adaptability of the proposed method, simulation experiments are conducted in the Beibu Gulf. Two layout scenarios—an appending layout with existing buoys and an independent layout without existing buoys—are designed to test the method’s adaptability under different deployment conditions. By combining Voronoi spatial partitioning and nearest-neighbor distance analysis, the optimized results are quantitatively evaluated in terms of spatial uniformity and observational effectiveness. The results indicate that the proposed method effectively enhances the spatial rationality and monitoring efficiency of buoy deployment, demonstrating strong generality and scalability. Full article

Addressing the current situation where in situ observations in the Arctic primarily target physical and a few biogeochemical parameters, leaving a gap in systematic direct observation of biological populations beneath sea ice, this study developed a polar ice-based ecological observation buoy system. Building upon conventional meteorological and oceanographic hydrographic sensors, this system innovatively integrates an underwater imaging module and key technologies such as machine learning-based automatic fish target recognition and reliable dual-channel satellite data transmission in polar environments. Its successful deployment during the 2025 15th Chinese National Arctic Research Expedition verified the system’s stability. During the initial one-month operation period (designed for a monitoring cycle of not less than one year), the data return rates for conventional and image data reached 100% and 96.8%, respectively, achieving quasi-real-time continuous observation of physical and ecological parameters at the air–sea interface in the Arctic Ocean, and it is capable of acquiring not only physical parameters but also visual observations of under-ice fauna. The system successfully acquired and transmitted images containing suspected biological targets and reference objects, providing the first in situ, image-based biological observation dataset for the central Arctic Ocean. This work establishes a new methodological capability for direct ecological monitoring, offering essential equipment support for quantifying biological presence, studying population dynamics, and informing evidence-based polar ecosystem governance. Full article

Calcareous tropical green macroalgae of the genus Halimeda are key reef-builders, yet the drivers of their diversification and population dynamics remain poorly understood. This study analyzed the species diversity of Halimeda in the Xisha (Paracel) Islands based on tufA gene sequences, focusing on evaluating the genetic diversity, population structure, and historical dynamics of two widespread species—Halimeda discoidea and Halimeda macroloba. The results indicate new records of Halimeda cylindracea and Halimeda cf. stuposa in the Xisha (Paracel) Islands. More importantly, H. discoidea and H. macroloba exhibited significantly different evolutionary histories. Specifically, H. discoidea showed a highly fragmented population structure, restricted gene flow, and a multimodal mismatch distribution, suggesting a complex historical process or long-term stability. In contrast, H. macroloba exhibited lower population differentiation, extensive gene flow, and non-significant neutrality test results, indicating long-term demographic stability without recent, drastic population events. Further validation based on gene flow analysis and divergence time estimation revealed that the lineage divergence of H. discoidea is older, while H. macroloba represents a lineage with a relatively younger evolutionary origin restricted to the Indo-Pacific region. This striking dichotomy clearly illustrates the interplay between intrinsic species-specific traits (e.g., dispersal capacity) and extrinsic historical factors (e.g., paleo-oceanographic events), leading to contrasting evolutionary outcomes among widespread marine taxa. By elucidating how differing evolutionary histories influence patterns of genetic diversity, this study provides a predictive framework for evaluating the resilience and guiding conservation priorities for critical marine calcifiers in the context of rapid environmental change. Full article

Sea surface wind speed is a crucial parameter for studying climate change and ocean dynamics. Accurate, real-time measurements are essential for meteorological and oceanographic observations. Global Navigation Satellite System Reflectometry (GNSS-R) is a key technology for sea surface wind speed retrieval. Existing wind speed retrieval models employ two primary approaches: unified modeling across the entire wind speed range and independent modeling for partitioned wind speed intervals. The former cannot effectively address physical property variations across wind speed ranges. The latter, while mitigating this issue, relies on empirical thresholds for interval partitioning that ignore actual data distribution and struggles to assign new samples to appropriate intervals during prediction. To address these limitations, this study employs the Gradient-Boosted Adaptive Multi-Objective Simulated Annealing (GAMSA) algorithm to construct a multi-objective optimization function and perform data-driven wind speed interval partitioning. Specialized XGBoost sub-models are then constructed for each partitioned interval, and their predictions are integrated through a stacking ensemble learning architecture. The experiments utilize a Cyclone Global Navigation Satellite System (CYGNSS) and ERA5 reanalysis data. The experimental results show that the proposed method reduces the root mean square error (RMSE) from 1.77 m/s to 1.43 m/s and increases the coefficient of determination (R²) from 0.6293 to 0.7770 compared with a global XGBoost model. It also exhibits enhanced accuracy under high wind speeds (>16 m/s) and, when independently validated with buoy data, achieves an RMSE of 1.52 m/s and R² of 0.79. The proposed method improves retrieval accuracy across both overall and individual wind speed intervals, avoids the sample isolation problem inherent in traditional empirical partitioning methods, and resolves the issue of assigning new samples to appropriate sub-models during application. Full article