Oceans

The prediction of ocean wave heights and periods is crucial for reducing maritime accidents. The application of neural networks to predictive modeling eliminates the need for complex parameter schemes that describe specific physical processes, thereby reducing computational costs. This study presents two single and three fusion models based on neural-network architectures: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), LSTM with an attention mechanism (LSTM+Attention), LSTM fused with a CNN (CNN+LSTM), and LSTM with Ensemble Empirical Mode Decomposition (EEMD+LSTM). The models were compared on multiple prediction lengths based on multivariate datasets from two oceanographic stations southeast of Fujian Province. All models performed well for prediction lengths of less than 24 h, with the coefficients of determination for wave height and period prediction higher than 0.7 and 0.6, respectively. For prediction lengths longer than 24 h, the predictive capability of EEMD+LSTM was superior to that of the other models. For a 48 h prediction length, the predictive results at each time step were compared with the true values across four typhoons. At the 24th and 48th time steps, phase lags were observed in the results from the CNN and LSTM. In contrast, EEMD+LSTM better captured the trends of the true values. Full article

To address the lack of compliance with the International Regulations for Preventing Collisions at Sea (COLREGs) in conventional timed elastic band (TEB)—based dynamic path planning for autonomous surface vessels (ASVs), this study proposes an intelligent path-planning framework that integrates an improved jump point search (JPS) algorithm with a COLREGs-aware TEB approach. At the global planning layer, an enhanced JPS algorithm incorporating redundant-node elimination and cubic Bezier curve smoothing is developed to construct the JPS–PB method, thereby reducing path redundancy and improving trajectory continuity. At the local planning layer, COLREGs encounter rules are reformulated as differentiable cost functions and embedded into the TEB optimization framework, enabling rule-compliant collision avoidance in typical maritime encounter scenarios, including head-on, crossing, and overtaking situations. Simulation results demonstrate that, compared with the conventional JPS algorithm, the proposed JPS-PB method reduces the path length by up to 8.6%, decreases the cumulative steering angle by 58.6%, and lowers the maximum steering angle by 88.6% on a 12 × 12 grid environment. Furthermore, compared with the conventional TEB algorithm, the proposed COLREGs–aware TEB (CTEB) increases the Distance at the Closest Point of Approach (DCPA) by 543%, 20.5%, 4.8%, and 13.8% in starboard-crossing encounters, head-on, overtaking, and special port-side-crossing encounter scenarios, respectively. Parameter sensitivity analyses further indicate that CTEB maintains stable collision-avoidance performance and rule-compliance capability under variations in the danger–distance threshold, danger–time threshold, and COLREGs cost weight. The results verify that the proposed JPS–PB+CTEB hybrid framework effectively improves path quality, dynamic obstacle-avoidance safety, and navigation-rule compliance for autonomous ASV operations. Full article

Satellite chlorophyll–a (Chla) products are widely used to study coastal productivity, but their performance often degrades in river-influenced and optically complex waters. We evaluated MODIS-Aqua Chla retrievals in the coastal upwelling area off central-southern Chile, a region strongly affected by seasonal river plumes, using monthly in situ Chla and hydrographic observations from Station 18 (August 2002 to September 2011), daily MODIS products, and matchup analyses based on 3 × 3 pixel windows and 1-, 3-, 5-, and 7-day composites. MODIS Chla and normalized Fluorescence Line Height (nFLH) reproduced the broad seasonal cycle, with maxima during spring–summer, but default MODIS Chla systematically exceeded in situ observations, particularly during periods of enhanced turbidity and river-influenced optical complexity. Among the raw satellite products, 1-day MODIS Chla matchups showed the strongest agreement with in situ Chla (r = 0.77, RMSE = 8.5 mg m⁻³), whereas 5-day composites increased matchup availability to 95% but reduced the correlation (r = 0.46, RMSE = 10.5 mg m⁻³). In contrast, nFLH showed more stable performance across composite lengths, although it underestimated high Chla values and should therefore be interpreted as a complementary fluorescence-based diagnostic rather than as a direct substitute for locally validated Chla retrievals. A gradient boosting model trained with MODIS remote-sensing reflectances improved the correspondence between satellite and in situ Chla relative to the default MODIS product within the available Station 18 matchup dataset. Because this model was evaluated using cross-validation rather than an independent regional validation dataset, the machine-learning results should be interpreted as a local proof of concept rather than a fully validated regional algorithm. These results indicate that standard MODIS algorithms overestimate Chla in this river-influenced upwelling system and highlight the value of local correction approaches, including machine-learning methods, for improving coastal ocean color products, provided that future applications include independent spatially distributed validation and improved bio-optical characterization of river-influenced waters. Full article

This work presents new data and several age–depth reconstructions based on distinct approaches to improve previously published age models for sedimentary sequences collected on the Iberian Margin middle shelf: PO287 6-1B, 2G (Porto); PO287 26-1B, 3G, and D13902 (Lisbon); and POPEI VC2B (Algarve). The new age models are constructed using radiocarbon dates calibrated with the IntCal20 calibration curve and smooth-spline regressions from the CLAM (non-Bayesian) and Bacon (Bayesian) models. A comparison of the age–depth models generated by the different approaches shows that, beyond the distinct solutions produced by CLAM and Bacon for each site, the differences between the two methods are inconsistent across sites. Furthermore, neither model yielded reliable results for the discontinuous sedimentary sequence with reworked older sediments and a hiatus. In this specific case, the “classical” linear regression (best-fit) approach appears to yield the results that enable intercomparison across all sequences. This exercise confirms the record of the 1755 earthquake and tsunami in the Lisbon core splice and a potential record of the same event in the Eastern Algarve, although the collected data indicate a more distal or lower-energy wave deposit. Full article

Over the past three decades, five satellites have succeeded one another on the reference orbit, building the longest continuous climate record of global sea level measurements. Its continuity is ensured thanks to tandem flights between consecutive satellites. In this paper, we demonstrate that the first satellite of the Sentinel-6 series (Sentinel-6 Michael Freilich) has enabled the detection of a processing anomaly in the Jason-1/2/3 ground segment. An inconsistency in the altimeter range reconstruction has been identified, causing its underestimation by 3.65 mm. At certain latitudes, determined by the satellite’s orbital velocity, the altimeter range shows no effect from the anomaly. For the reference orbit, the range is not affected at the poles, around the equator and, for ascending tracks, at 40° S. All Jason Geophysical Data Record (GDR) versions prior to GDR-G are impacted by the described processing anomaly. While a full reprocessing of the Jason data with the GDR-G standard is pending, this paper presents a latitudinal empirical correction to be applied to Jason datasets generated with GDR-F and earlier ground segments. This correction, to be applied on the altimeter range, is derived from one month of patched Jason-3 data and is intended for reference orbit only. Additionally, SWOT Nadir ground processing is also affected by the same processing error and has been corrected from the GDR-S2 version onward. Finally, our analysis shows a negligible impact of this processing anomaly on Jason Level-2-derived products, models and metrics. Full article

This study outlines a comprehensive analytical methodology applied to a vessel’s operational data to establish a rigorously validated, data-driven baseline for the Marine and Health Usage Monitoring System (MHUMS) algorithmic modules. By correlating a continuous three-month dataset of sensor telemetry with historical alarm logs, the primary objective was to develop and train a robust predictive model. This study addresses the critical challenge of converting continuous marine sensor telemetry into actionable early warnings by establishing a validated predictive baseline. The proposed baseline predictive architecture achieved a Recall of 0.924 and an area under the ROC curve (AUC) of 0.939 for a 15 min forward prediction window, proving the feasibility of transitioning from reactive diagnostic monitoring to proactive early warnings. Full article

Carukia barnesi is a small tropical Cubozoan that causes painful and potentially fatal stings (Irukandji syndrome) along the north-eastern coast of Australia during the summer monsoonal months between November and May. This research establishes the thermal and osmotic tolerance of the medusa stage of the Irukandji jellyfish C. barnesi to determine if environmental parameters influence the marked seasonality of this species. By quantifying oxygen consumption over a range of temperatures, the theoretical minimum thermal requirement for the long-term persistence of C. barnesi medusae (i.e., theoretical zero) was estimated at 21.5 °C, which does not explain the seasonal occurrence of this species. The highest bell contraction frequency was determined to fall between 27.5 °C and 30.9 °C, which encompassed the typical thermal regime in situ. This research concludes that the deleterious effects of reduced performance associated with a departure from suitable environmental temperatures may better describe the seasonal pattern of this species than the theoretical thermal limits. Conversely, departure from suitable thermal regimes did not explain the southern distribution limits of this species, suggesting that C. barnesi could theoretically persist further south than their loosely defined southern distribution limits. Salinity also had a significant effect on C. barnesi medusae whereby the highest bell contraction frequency occurred at salinity levels above 33.6‰. Salinity levels below 29‰ reduced swimming capacity and became lethal below 25‰. This result supports the hypothesis that C. barnesi medusae are oceanic and/or neritic and cannot persist in estuarine environments where low salinity conditions commonly occur. Furthermore, the respiration rate of C. barnesi was lower at night, providing further evidence that this species is less active during night conditions, presumably to conserve energy. Full article

Accurate prediction of significant wave height (SWH) is essential for fisheries management, coastal socioeconomic activities, and marine ecological conservation. In recent years, deep learning-based bias correction has shown considerable potential for improving numerical wave forecasts. However, many existing approaches are still constrained by limited receptive fields and often struggle to capture long-range spatiotemporal dependencies in wave forecast errors. To deal with this issue, we adapt and improve a video prediction framework, namely the Vision Mamba Recurrent Neural Network (VMRNN), to model and correct the spatiotemporal patterns of SWH prediction biases. Comprehensive evaluations show that the multi-channel VMRNN achieves consistently high predictive accuracy across different forecast lead times and sea-state conditions. When validated against reanalysis data, the proposed model reduces the root mean square error (RMSE) of WAVEWATCH III forecasts by 28.2%, 26.1%, and 24.7% at lead times of 24, 48, and 72 h, respectively. It also preserves the spatial structure of SWH fields quite well, with the spatial structural similarity index remaining as high as 0.945 even at the 72 h lead time. Regional assessments over high-wave areas further indicate that VMRNN can effectively reduce both the mean error and the systematic overestimation commonly found in numerical wave models. Additional validation using in situ buoys observations confirms that the model has a robust ability to correct systematic positive biases, especially for wave heights ranging from 0.5 m to 2 m. Taken together, these results suggest that VMRNN has strong spatiotemporal modeling capability and can serve as a promising post-processing framework for improving operational physics-based wave forecasting systems. Full article

Predicting a ship’s motion trend in waves is crucial for safe navigation and operation. Existing prediction models are mostly based on the assumption of local linear dynamics, which can achieve great performance in idealized ocean environments. However, ships typically sail in real marine environments with regular or irregular waves, which makes the robustness and real-time performance of ship motion estimation models particularly important. To address this limitation, this paper proposes a global linear predictor (GLP) based on the Koopman operator, which can effectively represent the nonlinear rolling dynamics of ships. Furthermore, the GLP model is used to predict and control the rolling motion of a ship in real time. The proposed method is validated in both regular and irregular wave environments. The simulation experiment results show that the accuracy of the proposed method is about 14% higher than that of other classical methods on ships’ rolling dynamics. And it achieves a more than 91% rolling reduction efficiency in all wave conditions, significantly decreasing the amplitude of a ship’s rolling. Full article

Coastal zones are facing rising exposure to climate-related hazards alongside intensifying human pressures, which highlights the need for robust tools to assess vulnerability. This study uses a GIS-based Coastal Vulnerability Index (CVI) to quantify and map relative vulnerability along ~13 km of shoreline in Al Hoceima Bay (northern Morocco). The proposed CVI integrates eight geological and physical indicators, including geomorphology, shoreline erosion and accretion rates, coastal slope, elevation, natural habitats, relative sea-level rise, significant wave height, and tidal range. Spatial analyses were performed using remote sensing data, historical records, field measurements, and Geographic Information Systems (GIS). The analysis reveals that 37% of the shoreline is categorized as high vulnerability, 44% is moderate, and 19% is low. Highly vulnerable sectors are primarily associated with low elevations, gentle coastal slopes, sandy beach systems, limited natural habitat protection, and proximity to river mouths. These findings demonstrate that the applied CVI provides a rapid and cost-effective framework for identifying priority areas for coastal management and climate adaptation. The proposed approach offers valuable decision-support insights for sustainable coastal planning in Al Hoceima Bay and other Mediterranean coastal environments characterized by limited data availability. Full article

To determine the factors influencing juvenile recruitment of the kelp Eisenia bicyclis, a seven-year monitoring survey was conducted in an area affected by seismic subsidence caused by the 2011 earthquake and subsequent breakwater restoration. Juvenile recruitment was high in September 2011 and in May to June of 2013–2015, but low in 2012, 2016 and 2017. Analysis of the relationship between environmental factors and juvenile recruitment revealed that recruitment was associated with light intensity, with lower water temperature two months prior, and an increase in nutrients four months prior. The seasonal increase in nutrient concentrations during winter may have been influenced by the seasonal northwestward coastal current. In contrast, despite elevated nutrient concentrations, recruitment was relatively poor in 2016–2017. This might be attributed to the unstable seabed environment associated with the breakwater construction. Our monthly monitoring of both the number of E. bicyclis juvenile recruitments and environmental factors at the same site demonstrated that a time-lagged increase in nutrient concentrations and a decrease in water temperature are associated with Eisenia bicyclis juvenile recruitment. This study provides fundamental information on kelp recruitment that will contribute to predicting the recovery of kelp communities following disturbances and recruitment dynamics under environmental change. Full article

The Amazon coastal zone exhibits remarkable habitat diversity and species richness, with nutrient-rich estuaries playing a crucial role in local food webs and supporting fish and other aquatic organisms. To examine the distribution of fish larvae and juveniles in the Taperaçu Estuary and their relationship with environmental variables, monthly sampling was conducted at two fixed stations in 2008. Samples were collected during flood and ebb spring tides using 500 μm mesh nets. In situ measurements of salinity, temperature, and dissolved oxygen were recorded, while pH and turbidity were determined in the laboratory. Abiotic variables did not differ significantly between tides, but salinity and dissolved oxygen were higher during the dry season. A total of 5175 individuals were identified, representing 17 families and 37 species. The ichthyoplankton community was dominated by Rhinosardinia amazonica, Anchovia clupeoides, Stellifer stellifer, and Microgobius meeki. Stations 1 and 2 showed differing abundance ranges, with higher values at station 1 during the rainy season. Preflexion stages were abundant at both stations, indicating the estuary’s importance as a nursery and development area for several fish species. Multivariate analyses revealed spatial and seasonal structuring of larval assemblages along the estuarine gradient, driven primarily by salinity, temperature, and turbidity. Our results emphasize the role of upper estuary sectors of eastern Amazonia as areas of spawning, larval development, and subsequent juvenile settlement, contributing to the dispersal of fish species throughout the estuary and adjacent coastal environments. The present findings also reinforce the ecological value of the studied Extractive Reserve and other protected areas along the Amazon littoral as essential habitats for larval refuge and development. The need for continued monitoring and preservation of these protected zones is evident. Full article

Accurately capturing the Kuroshio’s main axis and its multiscale frontal variations remains challenging due to the constraints of traditional fixed-section extraction methods. Here, we develop an adaptive iterative tracking algorithm utilizing high-resolution reanalysis data (2002–2024) that dynamically adjusts search directions and cross-sections via local velocity vectors, integrated with a dynamic step size and two-dimensional validation. Applying a multiscale variability decomposition framework across four key regions reveals distinct spatiotemporal dynamics. The North Equatorial Current bifurcation zone exhibits a significant strengthening trend driven by seasonal zonal and decadal meridional flows. Conversely, the Kuroshio east of Taiwan is dominated by high-frequency mesoscale processes (~70%) with a semi-annual cycle and no long-term trend. The East China Sea front maintains a highly stable seasonal meridional signal (25%). Crucially, the Luzon Strait intrusion shows a significant long-term weakening trend (~0.0029 m·s⁻¹·a⁻¹, p < 0.01), characterized by eastward strengthening and northward weakening, with ENSO significantly modulating its seasonal cycle. This approach substantially reduces systematic extraction errors compared to traditional fixed-section methods, as independently verified using satellite SST frontal gradients (median deviation < 0.2°), providing critical observational evidence for understanding western boundary current–marginal sea interactions and their dynamical responses under global warming. Full article

Wetland landscape classification is fundamental to monitoring changes in ecosystem patterns. This study proposes an ensemble classification approach that integrates Maximum Likelihood Classification (MLC) and Decision Tree (DT) methods with optimized feature selection for long-term wetland monitoring in the Liaohe Delta, China. Based on four periods of Landsat remote sensing images from 1987, 1997, 2007, and 2017, multi-dimensional features including PCA1, TCT, NDVI, and MNDWI were extracted to construct a hierarchical classification system comprising 13 landscape types. The results show that the integrated method achieved an overall classification accuracy of 87.71% and a Kappa coefficient of 0.85, improving by 16.50% and 19.72%, respectively, compared with the single MLC approach. The classification results reveal significant spatiotemporal variations in landscape patterns. Reed wetlands decreased from 1284.44 km² in 1987 to 1006.70 km² in 1997, followed by a recovery to 1275.53 km² in 2017. In contrast, Suaeda communities experienced severe degradation, declining sharply from 227.48 km² in 1987 to 30.52 km² in 2017. Meanwhile, the coastline advanced landward by 263.24 km², with the proportion of artificial shoreline increasing from 12.4% to 38.7%. The changes in wetland landscape types in the Liaohe Delta from 1987 to 2017 were mainly influenced by urbanization processes and ecological restoration policies. These findings indicate that the proposed method can effectively support long-term wetland landscape dynamics analysis and provide a useful reference for coastal wetland management. Full article

Holoplanktonic mollusks (Mollusca: Gastropoda: Pteropoda) are vital structural and functional components of marine zooplankton communities, characterized by high sensitivity to physicochemical shifts in the water column. Consequently, multidisciplinary assessments are essential to elucidate their community dynamics. This study investigated the epipelagic pteropod community structure in the deep-water basin of the southern Gulf of Mexico (sGoM) in relation to hydrographic features and circulation patterns. During the summer (September) of 2016, we collected high-resolution hydrographic data and zooplankton samples using CTD casts and oblique bongo net tows. Hydrographic data revealed intense temperature and density gradients, including a cold-dense core associated with a well-defined cyclonic eddy. The pteropod assemblage comprised 25 species from 13 genera and 10 families. Heliconoides inflatus (947.5 ind 100 m⁻³) and Limacina trochiformis (396.8 ind 100 m⁻³) were the most abundant species, whereas Cavolinia gibbosa (0.4 ind 100 m⁻³) and Cymbulia sp. (0.3 ind 100 m⁻³) were the least abundant. Horizontal distribution analyses revealed that the peak population densities occurred within the influence of the cyclonic eddy, particularly at its periphery where strong currents (>0.5 m/s) were recorded. A Canonical Correspondence Analysis identified temperature and salinity as the primary environmental drivers of community variability, while current systems significantly influenced the horizontal distribution of key species. Although pteropod research in the sGoM spans decades, most studies have been limited to taxonomic checklists, often overlooking environmental drivers and hydrographic influences. By applying a multidisciplinary approach to examine physical–biological coupling, this study advances the ecological understanding of this group within the historically underrepresented deep-water basin of the sGoM. Full article

We investigated the autumn-to-winter evolution of water-mass structure and nutrient concentrations in Funka Bay, southwestern Hokkaido, Japan, from October to February (2012–2019). Hydrographic and biogeochemical profiles show a recurrent seasonal transition from strongly stratified conditions in October, with low surface nutrients and bottom enrichment, to increasingly homogeneous distributions by mid-winter as vertical mixing intensifies. Depth-averaged nutrient concentrations generally decreased from October to December and increased from December to February, except during December 2015–February 2016. To assess whether February nutrient levels can be explained by Oyashio supply alone, we calculated February nutrient concentrations using a two-endmember mixing model (Oyashio endmember and December Funka Bay water) with an additional regeneration term that assumes nutrients consumed during the October–December autumn bloom were fully regenerated during December–February and redistributed by winter mixing. Under this framework, the expected February concentrations agreed with observations in all winters except 2015, when observed nutrients were lower than expected nutrients, consistent with additional biological drawdown after the early onset of the bloom by late February. These results indicate that the pre-bloom winter nutrient environment in Funka Bay is shaped by variable Oyashio intrusion superimposed on seasonal mixing and internal regeneration processes. Full article

The sound speed profile (SSP) is a core environmental parameter for underwater acoustic detection, navigation, communication, and other applications. However, its accurate acquisition is constrained by the sparsity of observational data and the ill-posed nature of inversion problems. This paper systematically reviews the research progress of SSP inversion under sparse observation constraints. The review traces the technical evolution from early physical models to current intelligent paradigms, classifies and compares mainstream inversion methods, presents typical application scenarios with quantitative case studies, provides a comparison of all kinds of SSP acquisition routes, and discusses critical challenges and future trends. The review reveals that current AI-driven methods achieve a practical accuracy of approximately 1–2 m/s but face bottlenecks in interpretability, cross-regional generalization, and extreme-condition robustness. Fusing physical constraints with multi-source sparse data (remote sensing, in-situ discrete measurements) emerges as the core direction for balancing inversion accuracy, efficiency, and cost. This paper provides a comprehensive reference for technical selection in marine acoustics, ocean observation, and underwater operations. Full article

Port State Control (PSC) inspections play a critical role in enforcing international maritime safety and environmental standards, yet little is known about how compliance behaviour interacts with economic cycles. This study examines the relationship between vessel detentions and freight market conditions using monthly data from the Paris and Tokyo Memoranda of Understanding (MoUs) over the period 2010–2021. A system of simultaneous equations is estimated using the Generalized Method of Moments (GMM) and Three-Stage Least Squares (3SLS) to address the bidirectional relationship between detention activity and freight market conditions, proxied by the Baltic Dry Index (BDI) and, for tanker specifications, the Baltic Dirty Tanker Index (BDTI). The results are consistent with a positive and statistically significant bidirectional relationship: vessel detentions increase during periods of strong freight market conditions, while past detentions are positively associated with freight rates, a pattern consistent with a signalling and sentiment channel. Institutional factors, including flag state quality, classification society affiliation, and ISM-related deficiencies, are also found to significantly influence detention risk, though their direction and magnitude vary across MoUs and vessel segments. These findings are consistent with the presence of opportunistic incentives during economic upswings, challenging the conventional expectation that stronger market conditions promote higher compliance. The study contributes to the literature by linking regulatory compliance with economic cycles and highlighting the importance of adaptive, risk-based enforcement strategies. It is important to note, however, that the aggregate nature of the data does not permit direct identification of firm-level behavioural mechanisms, and the findings should be interpreted as associational evidence consistent with these theoretical mechanisms. Full article

Marine oil spills pose critical challenges to environmental sustainability and socioeconomic stability. Taking four pivotal cases as the entry point, this study uses comparative case analysis, semi-structured stakeholder interviews, policy analysis and international gap comparison to systematically analyze the evolution of marine oil spill governance in the Taiwan region of China over two decades, aiming to identify systemic gaps and propose actionable reforms. By integrating and explicitly detailing these multiple methodologies, this research not only identifies but also systematically examines the Taiwan region of China’s unique challenges as a non-UN-member entity navigating international conventions like the international convention for the prevention of pollution from ships, 1973, as modified by the protocol of 1978 relating thereto (MARPOL 73/78). Key findings reveal persistent issues in decision-support tools, fragmented inter-agency coordination, and legal inadequacies in compensation mechanisms. The study’s novelty lies in its rigorous synthesis of localized case-driven insights compared with global best practices, proposing a concrete, phased model for a unified task force and context-aware, data-driven contingency plans to enhance real-time response efficiency. It further advocates for pragmatic steps to align the Taiwan region of China’s Marine Pollution Control Act with international standards while critically addressing the transboundary collaboration barriers imposed by its political status, exploring potential pathways through sub-national and regional partnerships. Notably, the 2023 Angel Container case underscores the urgency of modernizing enforcement capacities and integrating advanced technologies. By bridging gaps in governance, legal accountability, and practical international engagement, this research not only advances the Taiwan region of China’s preparedness but also offers a nuanced and adaptable blueprint for coastal regions facing similar geopolitical and environmental constraints. Its recommendations hold significant implications for global marine pollution management, emphasizing the interplay of policy innovation, technological adoption, and pragmatic cross-jurisdictional cooperation. Full article