Search Results (516)

High-resolution CTD profiles, with SVP cross-validation of the sound speed field, were recorded at a single station in the outer roadstead of the Port of Constanța (northwest Black Sea; 44°07′41″ N, 28°53′15″ E; depth ≈ 25 m; June 2024), revealing a strongly stratified, five-layer water column driven by three combined forcing mechanisms: seasonal thermal stratification with an abnormally shallow Cold Intermediate Water layer (7.3–15.6 m), Danube-sourced freshwater input, and anthropogenic disturbances consistent with port and anchorage activity. A contextual hypothesis is proposed that conflict-related marine traffic intensification may contribute to observed signals, but physical measurements cannot establish causation. At the main pycnocline (7.31–15.62 m), a density difference of Δρ = 4.02 kg m⁻³ yields a maximum Brunt–Väisälä frequency of N² = 2.37 × 10⁻³ s⁻², reducing vertical eddy diffusivity by two orders of magnitude (Kz ≈ 10⁻⁶ m² s⁻¹). Physical conditions—a shallow mixed layer (~0.7–1.2 m) and strong pycnocline—support the theoretical expectation of surface-layer contaminant accumulation; however, no chemical measurements were carried out to confirm contaminant presence. All contamination inferences rely exclusively on physical proxies (turbidity, dissolved oxygen, and density gradients), and contaminant retention remains untested for lack of direct chemical evidence. A dimensional Stratification-Controlled Retention Index (SCRI = N²/Kz; units: m⁻² s⁻¹) is introduced, and its consistency with the observed hydrographic structure is demonstrated. Full article

The deployment of offshore wind farms (OWFs) has increased impressively over the last decade. While a group of frontrunner countries has led early deployment, the offshore wind sector is expanding to new regions; the Thracian Sea represents a promising area for OWFs deployment due to its favorable wind and wave climate. The successful implementation of OWFs projects depends on a comprehensive understanding of local environmental conditions, with particular emphasis on complex wind–wave interactions quantification, as well as on robust and representative power performance evaluation. In the present paper, hourly environmental data spanning 29 years (1993–2021), including wind and wave parameters, are utilized to quantify joint probability distributions at selected four locations in the Thracian Sea. Corresponding environmental contours are derived and presented using a probabilistic model for given return period. The joint probability distributions of wind and wave conditions are estimated and the environmental contour surfaces for 50- and 100-year return periods are calculated and presented for generic use. Furthermore, the power production of an OWF comprising nine IEA 15 MW turbine units arranged in an orthogonal grid layout is assessed through a numerical model developed in an open access computational tool. The model accounts for key physical processes influencing OWF capacity performance, including wake interactions, atmospheric conditions, turbine control strategies, and layout effects. The results indicate a substantial value of annual energy production and capacity factor for different zones within Thracian Sea achieving a value of 526 GWh and 44%, respectively. The presented results provide practical guidance for OWFs development in the Thracian Sea and contributes to reducing uncertainty in early-stage project planning and future engineering studies. Full article

In the context of global climate warming, sea surface temperature (SST) rise and sea level (SL) rise are projected to amplify typhoon-related marine dynamic disaster risks. These are idealized sensitivity experiments designed to isolate the individual effects of SST warming and SL rise, not full climate projections. This study investigates Super Typhoon Doksuri (2023) using the WRF-SWAN-ROMS coupled model, with sensitivity experiments designed for SST (+0.8 °C, +2.0 °C, +3.5 °C) and SL rise (+0.4 m, +0.6 m, +0.8 m) scenarios referenced to IPCC AR6 projections. Results indicate that SST rise enhances typhoon intensity by approximately 16% at +3.5 °C, elevates mean wave height by 25.0%, and increases extreme significant wave height by 24.0%, with the extreme wave height sensitivity approximately 2.75 times that of the mean. Storm surge exhibits a nonlinear response, with the extreme surge sensitivity approximately 13.2 times that of the mean. SL rise has relatively minor effects on open sea areas but affects coastal regions notably, expanding the inundation area by approximately 47% under the 0.8 m scenario. The Taiwan Strait channeling effect amplifies wave heights and surges on the right side of the track. Comparative analysis suggests that SST indirectly amplifies disasters by enhancing typhoon intensity, while SL rise directly constrains nearshore dynamics through static water level elevation. These findings offer process-based insights into the contrasting physical mechanisms through which SST rise and SL rise affect coastal hazards in semi-enclosed regions and may inform future ensemble-based climate impact assessments. Full article

Unmanned Surface Vehicle (USV) clusters operating in maritime environments face dynamic communication conditions, including varying sea states, electromagnetic interference, and satellite denial, that render static communication topologies suboptimal. Existing approaches assess link quality through single indicators, typically the SNR, and lack mechanisms for automatic topology adaptation. This paper presents a multi-layer adaptive communication framework that achieves a mean communication quality score of 0.72 (vs. 0.51–0.66 for baselines), a message delivery rate of 94.1% under benign conditions, and a failure recovery time of 3.2 s (vs. 5.8–8.4 s for baselines) across five communication failure scenarios. The framework integrates three layers: a weighted multi-indicator communication quality metric fusing the SNR, packet loss rate, latency, and link stability into a unified score; a topology utility function that selects among centralized, distributed, and hierarchical topologies by optimizing a quality–threat–overhead objective; and a multi-modal backup communication manager with physics-based underwater acoustic, optical line-of-sight, and multi-hop relay fallback modes. Simulation results demonstrate consistent improvements over single-indicator and static-topology baselines, with particularly strong performance under satellite denial and jamming scenarios where multi-modal backup communication sustains delivery rates above 85% under simulated conditions. In summary, the framework demonstrates consistent improvements across all metrics (communication quality, delivery rate, recovery time) relative to four baselines, with the largest gains observed under the most challenging conditions (satellite denial and jamming). We emphasize that the framework adaptively selects among pre-defined canonical topologies (star, mesh, tree) based on real-time conditions rather than synthesizing optimal topologies de novo—a distinction between topology management and topology optimization. Full article

To address the “3R” issues (resistance, resurgence, and residue) associated with chemical control of the sea buckthorn fruit fly (R. batava obscuriosa), this study proposes a novel physical barrier technology aimed at reducing pesticide application intensity, mitigating environmental pollution, and enhancing fruit quality. Yellow sticky traps were deployed to monitor adult occurrence dynamics and delineate the critical control window, while black polyethylene (PE) ground cover was installed on the orchard floor around the base of sea buckthorn trunks to prevent adult emergence from the soil. Control efficacy was evaluated by comparing adult trap catches and fruit infestation rates between the black PE ground cover treatment and the untreated control. Monitoring results revealed that adult emergence commenced on 29 June, entered the peak period on 9 July, attained maximum trap catch on 24 July, and persisted into the late emergence phase through mid-to-late August. Control data demonstrated that mean trap catches in the black PE ground cover treatment were lower than those in the control. From 2024 to 2025, fruit infestation rates declined from 74.5% and 62.3% in the control plot to 19.0~22.0% and 16.2~19.3% in the treatment plots, respectively, with control efficacy consistently exceeding 65%. This study demonstrates that black PE ground cover reduces adult abundance and fruit infestation rates of R. batava obscuriosa, with control efficacy consistently exceeding 65%. The observed effects are consistent with a soil-surface barrier effect and likely attributed to dual physical mechanisms: it may reduce adult emergence from the soil into the canopy and may obstruct mature larvae from entering the soil to pupate. This technology represents an environmentally sound, sustainable green control option suitable for integration into IPM programs for the sea buckthorn industry. Full article

Conventional multi-mission altimetry fusion tends to attenuate short-wavelength sea surface height anomaly (SLA) signals when high-density two-dimensional SWOT observations are incorporated into a single smoothing framework. To address this limitation, this study proposes a scale-separated, scale-wise fusion framework for high-resolution SLA reconstruction that jointly exploits multi-mission nadir altimetry and SWOT wide-swath observations. Multi-mission Level-3 observations from Sentinel-3A/B, HY-2B, SARAL/Altika, and SWOT are first harmonized through quality control, spatiotemporal reference unification, and cross-calibration referenced to Jason-3; Jason-3 was not used as a fusion input; instead, it served as the cross-calibration reference and as an external validation source after excluding calibration-involved samples. The SWOT-observed SLA field is then decomposed using an 80 km Lanczos filter—chosen as a practical working scale reflecting SWOT’s effective resolution rather than a universal physical boundary—into a large-scale background component and a mesoscale–submesoscale perturbation component. The large-scale component is reconstructed using adaptive optimal interpolation with latitude-dependent covariance scales, whereas the mesoscale–submesoscale component is refined through a physically regularized Transformer-based learning branch that recovers organized sub-80 km variability as a relative enhancement with respect to the AVISO/CMEMS reference. The two components are finally recombined on a 0.08° × 0.08° grid to generate a global SLA product. Validation from August 2023 to August 2024 shows that the proposed product maintains strong large-scale consistency with AVISO/CMEMS, with a mean daily spatial correlation of approximately 0.85. Sample-independent cross-validation against concurrent Jason-3 along-track observations yields a mean daily RMSE of 4.9 cm. Regional case studies in the Kuroshio Extension and the Scotia Sea further show that, relative to a conventional unified fusion scheme, the proposed framework better preserves organized sub-80 km structures, including fronts, eddy boundaries, and filamentary features, without degrading the large-scale background. Two specific technical contributions are (i) a reproducible scale-separated workflow that decouples large-scale OI mapping from fine-scale learning-based reconstruction, and (ii) a physically regularized loss formulation that constrains spatial gradients and Laplacian smoothness to suppress nonphysical artifacts during small-scale enhancement. These results suggest that scale-separated fusion provides an effective and operationally practical strategy for next-generation high-resolution SLA products and for improved observation of dynamically significant short-wavelength ocean variability. Full article

As critical air-dropped acoustic sensors for underwater target detection, sonobuoys are frequently compromised by severe hydrodynamic self-noise induced by sea-surface wave excitation, which masks target signals and degrades detection performance. While structural optimizations have traditionally been employed, effective signal-processing-based noise suppression remains challenging because the noise is non-stationary and physically coupled with buoy motion. To address the limited physical interpretability of conventional decomposition methods, this study proposes a physically guided self-noise suppression framework: VMD Constrained by DCCA Correlation (VMD-DCCA). The main contribution is the incorporation of the Detrended Cross-Correlation Analysis (DCCA) coefficient between the sonobuoy’s vertical velocity and the acoustic data as a correlation-dependent constraint within the Variational Mode Decomposition (VMD) optimization process. This motion prior allows more targeted isolation of motion-induced components than standard data-driven decomposition. Simulation and controlled water-tank results show that VMD-DCCA outperforms EEMD and standard VMD, achieving an SNR improvement of approximately 15 dB at an input SNR of −9 dB. The reconstructed signal also preserves visible narrowband spectral lines in the time-frequency representation. These results demonstrate the potential of the proposed method for controlled or post-processing sonobuoy self-noise reduction, while validation under irregular open-ocean conditions remains necessary. Full article

Sea level rise poses a major threat to dry beach areas, particularly in low-lying and managed coastal environments. Reliable assessments of future beach vulnerability therefore require the combined consideration of sea level rise, tidal regime, meteorological forcing, and wave-driven processes. Here, a physically based methodology is applied to evaluate future inundation and beach response at five representative sandy beaches along the southern coast of Spain. The selected sites span mesotidal Atlantic and microtidal Mediterranean settings. The approach integrates present-day conditions with sea level rise projections under RCP 4.5 and RCP 8.5 scenarios, astronomical tide, and meteorological residuals. Wave run-up is estimated using the IH2VOF CFD (Computational Fluid Dynamics) model. Extreme still water levels and maximum inundation levels are derived for mid-century (2026–2045) and end-of-century (2081–2100) periods, and their impacts on available dry beach surface and beach width are quantified using cross-shore profiles. Results indicate a progressive reduction in dry beach surface and width across all sites, with impacts intensifying from mid- to end-century and from moderate to high-emission scenarios. While losses remain comparatively moderate under still-water assumptions, the inclusion of wave effects leads to substantially larger impacts. At the most vulnerable sites, dry beach surface losses reach up to 80% under still-water conditions, and up to complete loss (100%) when wave run-up is included, particularly along the mesotidal Atlantic coast. Overall, the results demonstrate that neglecting wave run-up can lead to a substantial underrepresentation of future beach inundation, and that its explicit inclusion provides a more reliable basis for beach management and adaptation planning under sea level rise. Full article

Located in the northern South China Sea (SCS), the Guangdong Sea areas exhibit a highly complex hydrodynamic structure driven by the combined effects of tides, monsoons, and offshore current systems, serving as a core region for China’s marine economy and offshore engineering. Although the Regional Ocean Modeling System (ROMS) is widely applied in current simulations, its accuracy is often constrained by the inadequate adaptability of its parameterization schemes to the regional environment. Furthermore, systematic parameter optimization tailored to this specific domain remains scarce. To address these limitations, this study conducts an observation-driven parameter optimization for surface current simulations in the western Guangdong Sea areas, aiming to enhance the reliability of hydrodynamic simulations and forecasting. A three-dimensional ROMS hydrodynamic model was employed to systematically design 18 physical parameterization experiments. The model’s performance was rigorously evaluated against 26 h continuous in situ current measurements from four observation stations, utilizing statistical metrics including the correlation coefficient (R), root mean square error (RMSE), Taylor diagrams, and the MMS standardized evaluation. The results indicate that the Mellor–Yamada vertical mixing scheme yields the optimal regional adaptability. For horizontal diffusion, the biharmonic scheme outperforms the Laplacian approach. Regarding bottom friction, the logarithmic formulation demonstrates superior accuracy compared to the quadratic and linear schemes, with the latter proven unsuitable for this region. A comprehensive evaluation identifies the ‘MY–Biharmonic–Logarithmic’ combination as the optimal parameterization configuration for the western Guangdong Sea areas. This study establishes an adaptable ROMS parameterization framework for the western Guangdong Sea areas and elucidates the influence mechanisms of key physical parameters on simulation outcomes. These findings not only provide high-precision hydrodynamic support for short-term pollutant dispersion forecasting, and disaster mitigation in this region but also offer valuable methodological references for numerical modeling in the broader SCS and analogous complex coastal environments. Full article

Submarine groundwater discharge (SGD) is an important pathway for water and solute exchange between coastal aquifers and the ocean, but its spatial detection remains challenging because field methods have limited coverage and remotely sensed anomalies may also reflect other coastal processes. This study developed a hydrodynamically constrained remote sensing framework for SGD identification by integrating optical and thermal indicators with hydrogeological constraints. Sentinel-2 imagery was used to derive the Normalized Difference Chlorophyll Index (NDCI) and Normalized Difference Turbidity Index (NDTI), while Landsat thermal data were used to quantify seasonal sea surface temperature variability using the 90th–10th percentile amplitude. These indicators were combined in a K-means clustering framework, and the classification results were further constrained using year-specific maximum offshore distances estimated from groundwater level observations with a Dupuit–Glover-based scaling approach and hydraulic time-lag correction. Applied to the north shore of Long Island, New York, the framework identified coherent nearshore SGD patches that were broadly consistent with field observation locations and showed both temporally persistent discharge zones and interannual variability in spatial extent. These results indicate that incorporating physically based constraints can improve the robustness and interpretability of remote sensing-based SGD detection. Full article

Organic matter production, recycling, and burial processes temporally fluctuate across the Clarion-Clipperton Zone (CCZ) in the Eastern Tropical Pacific. Between 2019 and 2022, we conducted pelagic and benthic surveys in Nauru Ocean Research Inc. contract area D (NORI-D) in the southeast CCZ to establish environmental baseline conditions. Here, we synthetise the natural ranges of variability in physicochemical and biogeochemical processes in NORI-D across multiple surveys and years. We present interannual water column physicochemical characteristics from five metocean and pelagic campaigns, annual satellite-derived net primary productivity and export production, time-integrated sediment trap annual particulate organic carbon flux, and seafloor biogeochemical and sediment physical characteristics from three benthic campaigns. Temperature and salinity seasonally varied at the sea surface. Strong thermohaline and oxygen stratification developed over 0–100 m. Mean net primary productivity, export production, and seafloor particulate organic carbon flux amounted to 634.1, 15.7, and 2.1 mg C m⁻² d⁻¹, respectively. These rates fluctuated nearly four-fold seasonally and interannually. An oxygen minimum zone (100–700 m) dampened organic carbon flux attenuation (b = −0.538) to the abyss. Abyssal seafloor organic matter dynamics showed more homogenous conditions in 2020–2021 (TOC = 0.57 ± 0.05%) than in 2022 (TOC = 0.42 ± 0.19%). Bioturbation rate and mixed-layer depth decreased from 2020 to 2022, while oxygen consumption increased at 0–1 cm bsf. Lipid consumption and compositional alteration in 2022 surpassed 2020–2021. Our findings provide critical baseline data to inform environmental impact assessments and monitoring programmes for deep-sea mining of polymetallic nodules in NORI-D. Full article

Rapid Arctic warming has significantly altered sea ice–atmosphere boundary layer processes, which low-resolution models struggle to resolve accurately. This study evaluates the historical performance (1958–2014) of four high-resolution models from CMIP6 HighResMIP—EC-Earth3P-HR, CNRM-CM6-1-HR, HadGEM3-GC3.1-HH, and Fgoals-f3-H—against ORAS5 and CMEMS reanalysis datasets and examines their physical response to rapid warming under the SSP5-8.5 scenario (2015–2025). Results show substantial intermodel differences in simulating Arctic sea ice thickness, mixed layer depth, sea surface temperature and salinity, and deep convection. HadG-EM3-GC3.1-HH and CNRM-CM6-1-HR perform best overall, reliably reproducing trends in the two major deep convection regions, meridional temperature–salinity gradients, and long-term evolution with lower biases and higher correlations. Under decadal strong warming, models generally simulate shoaling mixed layers in deep convection zones and upper-water destabilization in the Canada Basin, but responses in sea ice, eddy kinetic energy, and transect temperature–salinity vary markedly. HadGEM3-GC3.1-HH and CNRM-CM6-1-HR better represent physical quantities and ocean stratification consistent with observed real-world responses. We conclude that these two models are more suitable for studies of Arctic sea ice–atmosphere boundary layer changes and deep convection, providing a basis for high-resolution model selection and Arctic climate projection. Full article

Maritime aerial imaging is strongly affected by rapid illumination variations induced by dynamic sea conditions, which often cause conventional exposure control approaches to misinterpret intrinsic scene brightness as overexposure resulting from elevated camera settings. To overcome this issue, an adaptive exposure control framework based on a Glare-Aware Attention Network is proposed, implemented within an end-to-end dual-branch architecture. The framework utilizes an Exposure State Encoding (ESE) module to encode the current frame’s exposure parameters as conditional vectors, thereby resolving physical ambiguities in scene understanding. A Glare-Aware Spatial Attention (GASA) mechanism is further introduced, incorporating a glare prior map (GPM) generated using a “high-luminance, low-texture” heuristic to explicitly suppress sun glint effects. A Scene Difficulty-Adaptive Loss Weighting (SDAW) scheme is designed to adaptively regulate loss weights, and region-aware evaluation metrics, KREA and ISR, are defined. On a self-collected maritime aerial imaging dataset, the proposed approach significantly outperforms both traditional and deep learning-based methods in terms of full-frame and region-level performance metrics. Compared with the multi-task CNN baseline that has the closest parameter count, it achieves a 1.7 dB gain in PSNR. Cross-dataset validation on SeaDronesSee, temporal consistency analysis, and embedded platform testing further support the generalization and real-time feasibility of the proposed solution. Offering a high-accuracy, region-aware exposure control solution for aerial cameras in complex sea surface scenarios. Full article

Accurate spatiotemporal forecasting of sea surface temperature (SST) makes a great difference to offshore wind power development, since SST is a crucial factor influencing wind field patterns. In this work, a remote sensing-driven, multi-parameter field-coupled co-forecasting approach is proposed to utilize the cross-field interaction mechanisms among different physical fields to enhance forecasting performance. With this approach, more than one physical field can be simultaneously forecasted, thus improving forecasting efficiency. Compared with pure SST forecasting cases, the advanced enhancement of SST forecasting performance based on this approach is achieved by coupling SST with geostrophic current (GC) in data-driven forecasting. Also, both the spatiotemporal SST and GC fields are demonstrated to be accurately forecasted simultaneously. In addition, the causal effects between SST and GC are demonstrated as a reliable factor for evaluating the coupling scheme. To further improve co-forecasting performance, an exponential cross-entropy loss function is proposed for multi-physical field co-forecasting scenes, and shows more satisfying performance than a classical cross-entropy loss function. The results demonstrate that the data-driven multi-physical field-coupled co-forecasting approach is an advanced, highly efficient method that can accurately forecast more than one physical field at the same time. Full article

Accurate precipitation forecasting is essential for water resource management, flood early-warning systems, and agriculture, but remains difficult because of the nonlinear and highly variable spatiotemporal nature of rainfall. This paper compares four deep learning architectures—a standalone LSTM, a standalone CNN, a hybrid CNN–LSTM, and a Transformer encoder—against three classical baselines (persistence, day-of-year climatology, and per-grid-point ARIMA) for daily precipitation forecasting over Washington State at lead times of one to four days. A 40-year ERA5 dataset (1985–2024) of near-surface air temperature, mean sea-level pressure, and total precipitation is split into training (1985–2012), validation (2013–2015), and test (2016–2024) periods, with the test years held out completely. Each (model, horizon) is trained with three random seeds and evaluated in physical units (mm/day). On the held-out test period, the hybrid CNN–LSTM achieves the lowest RMSE at every horizon $h\ge 2$, with $R^2=0.576\pm 0.007$ and RMSE $=15.08\pm 0.07$ mm/day at $h=4$. Diebold–Mariano tests, paired t-tests, and bootstrap 95% confidence intervals confirm that the CNN–LSTM advantage over the LSTM is statistically significant at horizons 2–4 (but not at $h=1$), while CNN–LSTM is significantly better than every classical baseline and the Transformer at every horizon. The headline result is reproduced under a rolling-origin temporal cross-validation across three non-overlapping splits ($R^2\in [0.576,0.590]$). Practically, the sub-millisecond inference cost of the CNN–LSTM makes it directly deployable in operational forecasting pipelines used for flood early-warning, irrigation scheduling, and reservoir management, where even modest improvements in 3–4-day-ahead RMSE translate into measurable risk reduction and improved decision lead time for water managers and emergency planners. Full article