Current Articles
2025, Volume 47, Issue 12
Display Method:
2025,
47(12):
1-3.
Abstract:
2025,
47(12):
1-9.
doi: 10.12284/hyxb20250125
Abstract:
This paper focuses on the construction of the theoretical system of ocean technology. Based on an analysis of the characteristics of ocean technology, the definition of ocean technology is further clarified, and a classification is proposed, dividing it into fundamental ocean technologies, enabling ocean technologies, and applied ocean technologies. In-depth discussions are also carried out on some important aspects of ocean technology.
This paper focuses on the construction of the theoretical system of ocean technology. Based on an analysis of the characteristics of ocean technology, the definition of ocean technology is further clarified, and a classification is proposed, dividing it into fundamental ocean technologies, enabling ocean technologies, and applied ocean technologies. In-depth discussions are also carried out on some important aspects of ocean technology.
2025,
47(12):
103-113.
doi: 10.12284/hyxb20250115
Abstract:
Based on Sentinel-2 optical remote sensing imagery, this paper proposes a sea ice segmentation algorithm empowered by multi-dimensional attention within a U-Net architecture. Building upon the classical U-Net, the algorithm innovatively introduces a temporal-aware multi-head attention module at the end of the encoder path. This module enhances spatial perception using learnable spatial positional encodings and utilizes temporal encodings (where the year is processed by min-max normalization, and the month and day are encoded via sine-cosine functions) as query vectors to perform global temporal correlation reasoning on deep image features. Furthermore, a lightweight triple attention module (channel-spatial-temporal) is embedded within the decoder path. This module calculates the weights for these three dimensions and fuses feature information via element-wise multiplication, effectively enhancing key features and focusing on details. To validate the accuracy and effectiveness of the proposed algorithm, classical VIT, DeepLabV3+ and U-Net models were selected as comparative methods, and ablation studies were conducted. Experimental results demonstrate that the proposed algorithm achieves the best performance in terms of OA (Overall Accuracy), Kappa coefficient, and Mean IoU (Intersection over Union) coefficients, reaching 92.11%, 0.846, and 0.574, respectively. The combined effect of the two attention modules enables the model to avoid global bias while improving local classification confidence. Notably, the classification accuracy for 30%−50% ice concentration and fast ice was significantly improved by 48.8% and 31.95%, respectively.
Based on Sentinel-2 optical remote sensing imagery, this paper proposes a sea ice segmentation algorithm empowered by multi-dimensional attention within a U-Net architecture. Building upon the classical U-Net, the algorithm innovatively introduces a temporal-aware multi-head attention module at the end of the encoder path. This module enhances spatial perception using learnable spatial positional encodings and utilizes temporal encodings (where the year is processed by min-max normalization, and the month and day are encoded via sine-cosine functions) as query vectors to perform global temporal correlation reasoning on deep image features. Furthermore, a lightweight triple attention module (channel-spatial-temporal) is embedded within the decoder path. This module calculates the weights for these three dimensions and fuses feature information via element-wise multiplication, effectively enhancing key features and focusing on details. To validate the accuracy and effectiveness of the proposed algorithm, classical VIT, DeepLabV3+ and U-Net models were selected as comparative methods, and ablation studies were conducted. Experimental results demonstrate that the proposed algorithm achieves the best performance in terms of OA (Overall Accuracy), Kappa coefficient, and Mean IoU (Intersection over Union) coefficients, reaching 92.11%, 0.846, and 0.574, respectively. The combined effect of the two attention modules enables the model to avoid global bias while improving local classification confidence. Notably, the classification accuracy for 30%−50% ice concentration and fast ice was significantly improved by 48.8% and 31.95%, respectively.
2025,
47(12):
114-125.
doi: 10.12284/hyxb20250131
Abstract:
The disk-shaped submersible exhibits exceptional maneuverability, including zero-radius turning, precise landing, and stable hovering capabilities, which hold significant importance for enhancing the operational efficiency of seabed observation systems. However, research focusing on the hydrodynamic performance of disk-shaped submersibles remains limited. This study innovatively proposes a methodology integrating Planar Motion Mechanism (PMM) numerical experiments with the Routh criterion to evaluate the motion stability of disk-shaped submersibles. Firstly, the governing equations for submersible motion and motion stability criteria were derived. Subsequently, a numerical simulation model was established, and PMM-based numerical experiments were designed to calculate hydrodynamic coefficients. This research represents the first systematic comparison of hydrodynamic performance between HG1 and HG3 hull configurations in disk-shaped submersibles using the Routh criterion. The derived stability coefficients for horizontal and vertical motions demonstrate superior motion stability in the HG3 configuration. These findings were further validated through scale model basin experiments. The proposed numerical approach can be extended to motion stability analysis of various operational submersibles, effectively reducing the substantial costs associated with physical experiments while enhancing submersible performance in marine engineering applications.
The disk-shaped submersible exhibits exceptional maneuverability, including zero-radius turning, precise landing, and stable hovering capabilities, which hold significant importance for enhancing the operational efficiency of seabed observation systems. However, research focusing on the hydrodynamic performance of disk-shaped submersibles remains limited. This study innovatively proposes a methodology integrating Planar Motion Mechanism (PMM) numerical experiments with the Routh criterion to evaluate the motion stability of disk-shaped submersibles. Firstly, the governing equations for submersible motion and motion stability criteria were derived. Subsequently, a numerical simulation model was established, and PMM-based numerical experiments were designed to calculate hydrodynamic coefficients. This research represents the first systematic comparison of hydrodynamic performance between HG1 and HG3 hull configurations in disk-shaped submersibles using the Routh criterion. The derived stability coefficients for horizontal and vertical motions demonstrate superior motion stability in the HG3 configuration. These findings were further validated through scale model basin experiments. The proposed numerical approach can be extended to motion stability analysis of various operational submersibles, effectively reducing the substantial costs associated with physical experiments while enhancing submersible performance in marine engineering applications.
2025,
47(12):
10-24.
doi: 10.12284/hyxb20250109
Abstract:
Based on 0.1° × 0.1° high-resolution temperature, salinity, and three-dimensional velocity data from the Ocean general circulation model For the Earth Simulator (OFES), this study analyzes the applicability of the Omega equation for diagnosing vertical velocity in the South China Sea (SCS) and the spatiotemporal variation characteristics of vertical velocity in the SCS. The results show that the vertical velocity diagnosed by the Omega equation (wOmega) and the OFES model vertical velocity (wOFES) are of comparable magnitude in most areas of the SCS basin, approximately O(10−5 m/s), while wOmega is one order of magnitude smaller than wOFES on the northern continental shelf of the SCS. The spatial correlation coefficient (rs) between wOmega and wOFES is larger in the region southwestern of Taiwan (R1) and east of Vietnam (R2), and smaller in the western Philippines (R3), the southern SCS (R4), and the northeastern region of Hainan Island (R5). In terms of seasonal variation, rs is larger in winter and smaller in summer in regions R1, R2, and R4, while regions R3 and R5 show no significant seasonal characteristics in rs. Regions R1 and R2 are applicable areas for the Omega equation, where the temporal correlation coefficient rt between wOmega and wOFES is larger. In all regions, the contribution of the deformation term (\begin{document}$ {S}_{{\mathrm{DEF}}} $\end{document} ![]()
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Based on 0.1° × 0.1° high-resolution temperature, salinity, and three-dimensional velocity data from the Ocean general circulation model For the Earth Simulator (OFES), this study analyzes the applicability of the Omega equation for diagnosing vertical velocity in the South China Sea (SCS) and the spatiotemporal variation characteristics of vertical velocity in the SCS. The results show that the vertical velocity diagnosed by the Omega equation (wOmega) and the OFES model vertical velocity (wOFES) are of comparable magnitude in most areas of the SCS basin, approximately O(10−5 m/s), while wOmega is one order of magnitude smaller than wOFES on the northern continental shelf of the SCS. The spatial correlation coefficient (rs) between wOmega and wOFES is larger in the region southwestern of Taiwan (R1) and east of Vietnam (R2), and smaller in the western Philippines (R3), the southern SCS (R4), and the northeastern region of Hainan Island (R5). In terms of seasonal variation, rs is larger in winter and smaller in summer in regions R1, R2, and R4, while regions R3 and R5 show no significant seasonal characteristics in rs. Regions R1 and R2 are applicable areas for the Omega equation, where the temporal correlation coefficient rt between wOmega and wOFES is larger. In all regions, the contribution of the deformation term (
2025,
47(12):
25-34.
doi: 10.12284/hyxb20250133
Abstract:
Based on the measured high-frequency turbulence and salinity profile data from the South Passage of the Changjiang River Estuary in autumn 2020, how water salinity stratification modulates the turbulence viscosity coefficient, drag coefficient, and vertical velocity energy spectrum is quantified, to assess its influence on water-column turbulence. The observation station generally exhibits periodic stratification, with gradual transitions from mixing to stratification during the flood current, and from stratification to mixing during the ebb current. Salinity stratification significantly suppresses the intensity of turbulence in the bottom layer, leading to a decrease in the drag coefficient and viscosity coefficient of the bottom layer. Furthermore, as the height above the bed increases, the reduction in turbulence parameters caused by stratification becomes more pronounced. Spectral analyses of vertical velocity time series at multiple elevations indicate that stratification disproportionately damps low-frequency, large-scale eddies, and that the suppression strengthens away from the bed. Vertically, stratification reshapes the vertical eddy viscosity structure by lowering both the peak and the mean values, shifting the position where the peak occurs downward, and accelerating the decay above the peak, leading to an overall reduction in turbulence. The degree of turbulence suppression is negatively correlated with the height of the stratified region and positively correlated with the intensity of stratification.
Based on the measured high-frequency turbulence and salinity profile data from the South Passage of the Changjiang River Estuary in autumn 2020, how water salinity stratification modulates the turbulence viscosity coefficient, drag coefficient, and vertical velocity energy spectrum is quantified, to assess its influence on water-column turbulence. The observation station generally exhibits periodic stratification, with gradual transitions from mixing to stratification during the flood current, and from stratification to mixing during the ebb current. Salinity stratification significantly suppresses the intensity of turbulence in the bottom layer, leading to a decrease in the drag coefficient and viscosity coefficient of the bottom layer. Furthermore, as the height above the bed increases, the reduction in turbulence parameters caused by stratification becomes more pronounced. Spectral analyses of vertical velocity time series at multiple elevations indicate that stratification disproportionately damps low-frequency, large-scale eddies, and that the suppression strengthens away from the bed. Vertically, stratification reshapes the vertical eddy viscosity structure by lowering both the peak and the mean values, shifting the position where the peak occurs downward, and accelerating the decay above the peak, leading to an overall reduction in turbulence. The degree of turbulence suppression is negatively correlated with the height of the stratified region and positively correlated with the intensity of stratification.
2025,
47(12):
35-47.
doi: 10.12284/hyxb20250137
Abstract:
Qinzhou Bay is a typical estuary–bay coupled system. Seabed surface sediments distribution pattern in the bay and their variations can substantially influence navigation-channel geomorphological stability and vessel safety. Based on high-density seabed surface sediment samples and hydrological data collected in Qinzhou Bay during the flood and dry seasons of 2024, this study systematically investigates the seasonal variability of seabed surface sediments and the driving mechanisms. The results show that seabed surface sediments in Qinzhou Bay are dominated by sand. The mean grain size is 3.67φ (0.079 mm) in the flood season and 3.39φ (0.095 mm) in the dry season, indicating a coarser tendency in the dry season. During the flood season, six sediment types are identified, including medium sand, fine sand, very fine sand, coarse silt, medium silt and fine silt, with silts and fine sand dominating the inner bay, fine sand dominating the northern Longmen Channel, medium sand dominating the southern outlet and the central outer bay while sediments on both sides of the eastern and western navigation channels turning finer again. During the dry season, five sediment types are identified, including medium sand, fine sand, very fine sand, coarse silt and medium silt. Fine sand dominates the inner bay, the Longmen Channel and the western outer bay, medium sand dominates the central outer bay and sediments in the eastern outer bay become finer. The distribution of seabed surface sediment in Qinzhou Bay is characterized by "fine in the inner bay, coarse in the channel, coarse in the central outer bay and fine on the two sides", which is consistent in flood and dry seasons, as shown in the first mode of Empirical Orthogonal Function (EOF). However, the second mode of EOF suggest different characters in the flood and dry seasons. Specifically, the flood season is characterized by "fine sand dominating the central–eastern outer bay" while the dry season is characterized by "very fine sand dominating the inner bay, fine sand and coarse silt dominating the western and eastern sides of the outer bay, respectively". The seabed surface sediment distribution in Qinzhou Bay is primarily controlled by the coupled effects of runoff and tidal currents, while local sedimentation differences are jointly influenced by channel constrained flow effects and human activities such as channel dredging, canal construction and aquaculture industry.
Qinzhou Bay is a typical estuary–bay coupled system. Seabed surface sediments distribution pattern in the bay and their variations can substantially influence navigation-channel geomorphological stability and vessel safety. Based on high-density seabed surface sediment samples and hydrological data collected in Qinzhou Bay during the flood and dry seasons of 2024, this study systematically investigates the seasonal variability of seabed surface sediments and the driving mechanisms. The results show that seabed surface sediments in Qinzhou Bay are dominated by sand. The mean grain size is 3.67φ (0.079 mm) in the flood season and 3.39φ (0.095 mm) in the dry season, indicating a coarser tendency in the dry season. During the flood season, six sediment types are identified, including medium sand, fine sand, very fine sand, coarse silt, medium silt and fine silt, with silts and fine sand dominating the inner bay, fine sand dominating the northern Longmen Channel, medium sand dominating the southern outlet and the central outer bay while sediments on both sides of the eastern and western navigation channels turning finer again. During the dry season, five sediment types are identified, including medium sand, fine sand, very fine sand, coarse silt and medium silt. Fine sand dominates the inner bay, the Longmen Channel and the western outer bay, medium sand dominates the central outer bay and sediments in the eastern outer bay become finer. The distribution of seabed surface sediment in Qinzhou Bay is characterized by "fine in the inner bay, coarse in the channel, coarse in the central outer bay and fine on the two sides", which is consistent in flood and dry seasons, as shown in the first mode of Empirical Orthogonal Function (EOF). However, the second mode of EOF suggest different characters in the flood and dry seasons. Specifically, the flood season is characterized by "fine sand dominating the central–eastern outer bay" while the dry season is characterized by "very fine sand dominating the inner bay, fine sand and coarse silt dominating the western and eastern sides of the outer bay, respectively". The seabed surface sediment distribution in Qinzhou Bay is primarily controlled by the coupled effects of runoff and tidal currents, while local sedimentation differences are jointly influenced by channel constrained flow effects and human activities such as channel dredging, canal construction and aquaculture industry.
2025,
47(12):
48-59.
doi: 10.12284/hyxb20250135
Abstract:
Since the artificial diversion of the Huanghe River into the Bohai Sea via the northern channel in 1996, the estuary sand spit has exhibited a distinct northward migration trend. Current research on estuarine evolution primarily focuses on the interplay between fluvial water-sediment inputs and marine hydrodynamic forces, while a systematic understanding of the role of the Coriolis force as a persistent driver remains lacking. Through hydrodynamic numerical modeling, this study investigates the influence of the Earth’s Coriolis force on tidal current structures and sediment transport patterns in the nearshore region of the Huanghe River Delta by comparing scenarios with and without Coriolis effects. The simulation shows that the Coriolis force drives the nearshore tidal current to move in a reciprocating manner, and forms a closed elliptical high velocity area (velocity > 0.8 m/s) outside the estuary. At the same time, the M2 tidal amphidromic point is formed near the No.5 pile on the north side of the estuary, and the sediment diffusion range on the north side of the mouth is significantly larger than that without Coriolis force. In the absence of Coriolis force, the nearshore tidal current is mainly radial reciprocating motion, and there is no closed high velocity area outside the estuary. The amphidromic tide point around the No.5 pile also disappears, and the longitudinal diffusion range of estuarine sediment to the open sea is larger. These findings indicate that the Coriolis force enhances lateral sediment transport by intensifying the transverse movement of flood and ebb currents. The formation of the M2 amphidromic point, acting as a “low potential energy zone”, increases the potential energy gradient between the estuary and the amphidromic point, thereby strengthening northward sediment transport during ebb tides. Furthermore, the increased indentation of the northern bayline weakens local tidal dynamics, promoting additional sediment deposition in this area. The synergistic effects of these mechanisms collectively drive the northward evolutionary process of the Huanghe River estuary sand spit.
Since the artificial diversion of the Huanghe River into the Bohai Sea via the northern channel in 1996, the estuary sand spit has exhibited a distinct northward migration trend. Current research on estuarine evolution primarily focuses on the interplay between fluvial water-sediment inputs and marine hydrodynamic forces, while a systematic understanding of the role of the Coriolis force as a persistent driver remains lacking. Through hydrodynamic numerical modeling, this study investigates the influence of the Earth’s Coriolis force on tidal current structures and sediment transport patterns in the nearshore region of the Huanghe River Delta by comparing scenarios with and without Coriolis effects. The simulation shows that the Coriolis force drives the nearshore tidal current to move in a reciprocating manner, and forms a closed elliptical high velocity area (velocity > 0.8 m/s) outside the estuary. At the same time, the M2 tidal amphidromic point is formed near the No.5 pile on the north side of the estuary, and the sediment diffusion range on the north side of the mouth is significantly larger than that without Coriolis force. In the absence of Coriolis force, the nearshore tidal current is mainly radial reciprocating motion, and there is no closed high velocity area outside the estuary. The amphidromic tide point around the No.5 pile also disappears, and the longitudinal diffusion range of estuarine sediment to the open sea is larger. These findings indicate that the Coriolis force enhances lateral sediment transport by intensifying the transverse movement of flood and ebb currents. The formation of the M2 amphidromic point, acting as a “low potential energy zone”, increases the potential energy gradient between the estuary and the amphidromic point, thereby strengthening northward sediment transport during ebb tides. Furthermore, the increased indentation of the northern bayline weakens local tidal dynamics, promoting additional sediment deposition in this area. The synergistic effects of these mechanisms collectively drive the northward evolutionary process of the Huanghe River estuary sand spit.
2025,
47(12):
60-69.
doi: 10.12284/hyxb20250119
Abstract:
The stability and safety of floating offshore wind turbine (FOWT) platforms in deep-sea and far-sea environments are crucial for the entire system. Currently, the stochastic design wave method is the conventional approach for structural design; however, its assumption that the maximum structural response follows a Rayleigh distribution may not accurately reflect reality. To address this, this paper proposes an improved stochastic design wave method that reasonably considers the stochastic characteristics of extreme structural responses. Specifically, this method establishes a probability model for the local short-term maximum distribution using samples of the maximum structural response derived from the mean zero-crossing period. Subsequently, a global probability model for the maximum value over the total duration is derived to determine the design wave parameters. A 5 MW Braceless FOWT is selected as the case study for a comparative analysis of wave loads and stresses using both the conventional and improved methods. The results indicate that the improved method more accurately characterizes the stochastic characteristics of extreme responses, and thus the calculated structural stress aligns better with actual conditions. Notably, the conventional stochastic design wave method is found to underestimate the structural stress with a maximum error of 4.63%, which implies that structures designed with this approach may pose potential safety hazards. The findings of this study have significant implications for the structural design and safety assessment of similar FOWT platforms.
The stability and safety of floating offshore wind turbine (FOWT) platforms in deep-sea and far-sea environments are crucial for the entire system. Currently, the stochastic design wave method is the conventional approach for structural design; however, its assumption that the maximum structural response follows a Rayleigh distribution may not accurately reflect reality. To address this, this paper proposes an improved stochastic design wave method that reasonably considers the stochastic characteristics of extreme structural responses. Specifically, this method establishes a probability model for the local short-term maximum distribution using samples of the maximum structural response derived from the mean zero-crossing period. Subsequently, a global probability model for the maximum value over the total duration is derived to determine the design wave parameters. A 5 MW Braceless FOWT is selected as the case study for a comparative analysis of wave loads and stresses using both the conventional and improved methods. The results indicate that the improved method more accurately characterizes the stochastic characteristics of extreme responses, and thus the calculated structural stress aligns better with actual conditions. Notably, the conventional stochastic design wave method is found to underestimate the structural stress with a maximum error of 4.63%, which implies that structures designed with this approach may pose potential safety hazards. The findings of this study have significant implications for the structural design and safety assessment of similar FOWT platforms.
2025,
47(12):
70-83.
doi: 10.12284/hyxb20250121
Abstract:
Accurate assessment of a marine structure’s long-term extreme response is fundamental to its survivability, yet an unclear taxonomy of external environmental conditions hampers such assessments. While many studies prioritize wind and wind sea, real sea states are frequently multimodal, with wind sea and swell superposed. Unimodal-spectrum time-series methods cannot represent this multimodality or the statistical dependence among wind, wind sea, and swell, leading to underestimated joint extremes and biased reliability/safety evaluations. Swell—a low-frequency component whose intensity can rival wind sea—readily excites low-frequency resonance in flexible systems such as offshore wind turbines, amplifying dynamic responses and cumulative fatigue. Recent standards (IEC 61400-3-2: 2025 and China’s guideline for integrated analysis of floating offshore wind turbines) explicitly require swell to be treated as a mandatory load case. Accordingly, we treat swell as a co-equal hazard with wind and wind sea. Using reanalysis data from representative stations in the South China Sea, East China Sea, Bohai Sea, and Yellow Sea, we build a joint probabilistic model of the three drivers and, via correlation analysis, Granger causality tests, and conditional probability analysis, reveal region-specific dependence structures. For the South China Sea, the environmental contour method is then used to construct an extreme-environment model that explicitly includes swell. Results show that incorporating swell markedly increases the complexity of environmental-variable combinations; omitting it distorts the environmental model and underestimates extremes. By extending the conventional wind–wave framework to include swell and demonstrating its necessity as a hazard, the study clarifies condition categories and supplies a more complete and accurate environmental input for long-term extreme-response assessment.
Accurate assessment of a marine structure’s long-term extreme response is fundamental to its survivability, yet an unclear taxonomy of external environmental conditions hampers such assessments. While many studies prioritize wind and wind sea, real sea states are frequently multimodal, with wind sea and swell superposed. Unimodal-spectrum time-series methods cannot represent this multimodality or the statistical dependence among wind, wind sea, and swell, leading to underestimated joint extremes and biased reliability/safety evaluations. Swell—a low-frequency component whose intensity can rival wind sea—readily excites low-frequency resonance in flexible systems such as offshore wind turbines, amplifying dynamic responses and cumulative fatigue. Recent standards (IEC 61400-3-2: 2025 and China’s guideline for integrated analysis of floating offshore wind turbines) explicitly require swell to be treated as a mandatory load case. Accordingly, we treat swell as a co-equal hazard with wind and wind sea. Using reanalysis data from representative stations in the South China Sea, East China Sea, Bohai Sea, and Yellow Sea, we build a joint probabilistic model of the three drivers and, via correlation analysis, Granger causality tests, and conditional probability analysis, reveal region-specific dependence structures. For the South China Sea, the environmental contour method is then used to construct an extreme-environment model that explicitly includes swell. Results show that incorporating swell markedly increases the complexity of environmental-variable combinations; omitting it distorts the environmental model and underestimates extremes. By extending the conventional wind–wave framework to include swell and demonstrating its necessity as a hazard, the study clarifies condition categories and supplies a more complete and accurate environmental input for long-term extreme-response assessment.
2025,
47(12):
84-93.
doi: 10.12284/hyxb20250113
Abstract:
Estimating the maximum individual wave overtopping discharge based on the Weibull distribution crucially depends on the accurate determination of its parameters—namely, the shape parameter and the overtopping percentage. Existing research has primarily focused on deep and intermediate water depth conditions, with a lack of systematic analysis on the characteristics of these distribution parameters within the surf zone. This study extends the experimental range of relative water depths to 0.9−4, covering conditions in the surf zone, intermediate depths, and deep water. It specifically investigates the influence of four dimensionless variables—relative water depth, relative crest freeboard, wave steepness, and seabed slope—on the distribution parameters, and establishes a parameter estimation method applicable to these extended conditions. Experimental results indicate that within the range covering the surf zone, both the shape parameter and the overtopping percentage exhibit a unimodal characteristic, resembling the form of solitary waves, as the relative water depth changes. Based on this observation, the study draws on the solitary-wave-like functional form to formulate calculation formulas for the distribution parameters, thereby enabling the prediction of the maximum individual wave overtopping discharge. Compared to existing models, the proposed method demonstrates lower prediction errors across the experimental range, with its advantages being particularly significant in the shallow water regions of the surf zone.
Estimating the maximum individual wave overtopping discharge based on the Weibull distribution crucially depends on the accurate determination of its parameters—namely, the shape parameter and the overtopping percentage. Existing research has primarily focused on deep and intermediate water depth conditions, with a lack of systematic analysis on the characteristics of these distribution parameters within the surf zone. This study extends the experimental range of relative water depths to 0.9−4, covering conditions in the surf zone, intermediate depths, and deep water. It specifically investigates the influence of four dimensionless variables—relative water depth, relative crest freeboard, wave steepness, and seabed slope—on the distribution parameters, and establishes a parameter estimation method applicable to these extended conditions. Experimental results indicate that within the range covering the surf zone, both the shape parameter and the overtopping percentage exhibit a unimodal characteristic, resembling the form of solitary waves, as the relative water depth changes. Based on this observation, the study draws on the solitary-wave-like functional form to formulate calculation formulas for the distribution parameters, thereby enabling the prediction of the maximum individual wave overtopping discharge. Compared to existing models, the proposed method demonstrates lower prediction errors across the experimental range, with its advantages being particularly significant in the shallow water regions of the surf zone.
2025,
47(12):
94-102.
doi: 10.12284/hyxb20250107
Abstract:
A two-dimensional numerical wave flume was developed using the open-source computational fluid dynamics platform OpenFOAM and its waves2Foam wave generation toolbox to simulate wave propagation over oyster reef ecological bottom protection under both regular and irregular wave conditions. The Volume of Fluid (VoF) method was employed to capture the free surface, while the k-ω SST turbulence model was used to resolve near-wall flow and energy dissipation processes. The model’s reliability in reproducing wave propagation characteristics was verified through comparison with physical model experiments. Parametric analyses were then conducted to examine the effects of curvature-based roughness coefficient (Cr), incident wave height (Hs), and reef flat water depth (hr) on wave attenuation performance. Results show that roughness is the key factor controlling wave dissipation: when Cr > 0.2, the wave transmission coefficient under irregular waves decreases by 37%–42% compared with a smooth bed. Higher incident wave heights markedly enhance energy dissipation, whereas greater reef flat water depth weakens the dissipation effect of the rough surface. These findings provide quantitative guidance for optimizing the design of oyster reef ecological bottom protection structures and their application in coastal defense engineering.
A two-dimensional numerical wave flume was developed using the open-source computational fluid dynamics platform OpenFOAM and its waves2Foam wave generation toolbox to simulate wave propagation over oyster reef ecological bottom protection under both regular and irregular wave conditions. The Volume of Fluid (VoF) method was employed to capture the free surface, while the k-ω SST turbulence model was used to resolve near-wall flow and energy dissipation processes. The model’s reliability in reproducing wave propagation characteristics was verified through comparison with physical model experiments. Parametric analyses were then conducted to examine the effects of curvature-based roughness coefficient (Cr), incident wave height (Hs), and reef flat water depth (hr) on wave attenuation performance. Results show that roughness is the key factor controlling wave dissipation: when Cr > 0.2, the wave transmission coefficient under irregular waves decreases by 37%–42% compared with a smooth bed. Higher incident wave heights markedly enhance energy dissipation, whereas greater reef flat water depth weakens the dissipation effect of the rough surface. These findings provide quantitative guidance for optimizing the design of oyster reef ecological bottom protection structures and their application in coastal defense engineering.
2025,
47(12):
126-135.
doi: 10.12284/hyxb20250127
Abstract:
The Indian Ocean Dipole (IOD) is the dominant climate mode in the tropical Indian Ocean, characterized by an east–west dipole pattern in sea-surface temperature anomalies that exerts substantial influence on regional and global climate variability. Current IOD forecasting methods predominantly rely on multivariate coupled or traditional statistical models, which pose significant challenges such as high computational complexity and multivariate noise interference. To address these challenges, this study proposes a cuboid attention-based IOD prediction model(CAIPM). The model takes sea surface temperature anomalies as the sole input variable, incorporates a Spatio-Temporal Gradient Enhancement Module (STGEM), which integrates sliding windows, temporal difference, and spatial convolution operations to enhance spatiotemporal feature extraction. By effectively capturing spatiotemporal dependencies within the Sea Surface Temperature Anomaly (SSTA) field through a cuboid attention mechanism, it directly outputs future spatiotemporal SSTA predictions, from which the IOD index is subsequently calculated. Experimental results demonstrate that the CAIPM significantly outperforms traditional statistical methods and current mainstream deep learning models in predicting the IOD index. Specifically, for a 12-month lead prediction, the Pearson correlation coefficient (PCC) of CAIPM is 32%, 22%, 16%, and 6% higher than that of the CNN, CNN-LSTM, TCN, and ConvLSTM models, respectively.
The Indian Ocean Dipole (IOD) is the dominant climate mode in the tropical Indian Ocean, characterized by an east–west dipole pattern in sea-surface temperature anomalies that exerts substantial influence on regional and global climate variability. Current IOD forecasting methods predominantly rely on multivariate coupled or traditional statistical models, which pose significant challenges such as high computational complexity and multivariate noise interference. To address these challenges, this study proposes a cuboid attention-based IOD prediction model(CAIPM). The model takes sea surface temperature anomalies as the sole input variable, incorporates a Spatio-Temporal Gradient Enhancement Module (STGEM), which integrates sliding windows, temporal difference, and spatial convolution operations to enhance spatiotemporal feature extraction. By effectively capturing spatiotemporal dependencies within the Sea Surface Temperature Anomaly (SSTA) field through a cuboid attention mechanism, it directly outputs future spatiotemporal SSTA predictions, from which the IOD index is subsequently calculated. Experimental results demonstrate that the CAIPM significantly outperforms traditional statistical methods and current mainstream deep learning models in predicting the IOD index. Specifically, for a 12-month lead prediction, the Pearson correlation coefficient (PCC) of CAIPM is 32%, 22%, 16%, and 6% higher than that of the CNN, CNN-LSTM, TCN, and ConvLSTM models, respectively.
2025,
47(12):
136-149.
doi: 10.12284/hyxb20250111
Abstract:
This study aims to enhance wave forecast accuracy and model generalization in the South China Sea island and reef waters using a BO-LSTM model. We systematically investigated the effects of input factors, model cross-station transferability, and multi-step prediction performance. The research employed single-factor (historical wave height) and dual-factor (historical wave height + wind speed) input schemes. Forecasts for 1−24 h were generated and validated at four stations (Qilianyu, Ganquan Island, Jinqing Island, and Huaxia Shoal) using the Rolling Forecast (RF) and Direct Multi-step (DM) methods. Results show that model performance is highly correlated with the hydrodynamic environment dictated by station geomorphology. The model trained on data from Qilianyu Station, with its “semi-sheltered to semi-open” setting, demonstrated the strongest and most stable generalization ability (optimal window n = 2) and excellent cross-station performance. In contrast, models for the “localized lagoon” stations (Ganquan Island, Jinqing Island) and the “open water” station (Huaxia Shoal) exhibited limited transferability due to significant “data domain shift” arising from their distinct geographic settings. For short-term forecasts, historical wave height was the core input factor, but its dominance showed geographic dependence. Its weight was significantly higher (>1.7 times) than wind speed at Qilianyu and Jinqing Island, while wind speed contribution was greater (advantage ratio <1.4) at Ganquan Island and Huaxia Shoal. For multi-step forecasting, “DM + Dual” performed best for short-to-medium terms (1−18 h), whereas “RF + Dual” was superior for long-term forecasts (19−24 h) and at Ganquan Island across all horizons. This study validates BO-LSTM’s effectiveness for wave forecasting in the South China Sea and provides physically interpretable insights for developing regional intelligent forecasting models by linking data-driven patterns with geophysical mechanisms.
This study aims to enhance wave forecast accuracy and model generalization in the South China Sea island and reef waters using a BO-LSTM model. We systematically investigated the effects of input factors, model cross-station transferability, and multi-step prediction performance. The research employed single-factor (historical wave height) and dual-factor (historical wave height + wind speed) input schemes. Forecasts for 1−24 h were generated and validated at four stations (Qilianyu, Ganquan Island, Jinqing Island, and Huaxia Shoal) using the Rolling Forecast (RF) and Direct Multi-step (DM) methods. Results show that model performance is highly correlated with the hydrodynamic environment dictated by station geomorphology. The model trained on data from Qilianyu Station, with its “semi-sheltered to semi-open” setting, demonstrated the strongest and most stable generalization ability (optimal window n = 2) and excellent cross-station performance. In contrast, models for the “localized lagoon” stations (Ganquan Island, Jinqing Island) and the “open water” station (Huaxia Shoal) exhibited limited transferability due to significant “data domain shift” arising from their distinct geographic settings. For short-term forecasts, historical wave height was the core input factor, but its dominance showed geographic dependence. Its weight was significantly higher (>1.7 times) than wind speed at Qilianyu and Jinqing Island, while wind speed contribution was greater (advantage ratio <1.4) at Ganquan Island and Huaxia Shoal. For multi-step forecasting, “DM + Dual” performed best for short-to-medium terms (1−18 h), whereas “RF + Dual” was superior for long-term forecasts (19−24 h) and at Ganquan Island across all horizons. This study validates BO-LSTM’s effectiveness for wave forecasting in the South China Sea and provides physically interpretable insights for developing regional intelligent forecasting models by linking data-driven patterns with geophysical mechanisms.
2025,
47(12):
150-164.
doi: 10.12284/hyxb20250129
Abstract:
Traditional harmonic analysis based on the ordinary least squares (OLS) method is sensitive to noise and susceptible to contamination by measurement errors and strong non-tidal processes. Harmonic analysis utilizing the iteratively reweighted least squares (IRLS) method reduces the influence of outliers by assigning them smaller weights, thereby effectively improving accuracy and stability compared to the OLS method. However, a systematic comparison of the precision of these two methods in tidal-level analysis within tidal rivers is still lacking. This study systematically compares the two methods using measured water level data from the Qiantang River in Zhejiang Province, China, through both idealized and practical experiments. The results indicate that: (1) For short time series (<3 months), the IRLS method yields more accurate results than OLS, with the mean vector difference reduced by over 2 cm, while the difference between the two methods diminishes as the time series lengthens (>3 months). (2) In the lower reaches of the Qiantang River, the difference between the two methods is minimal. However, in the middle to upper reaches (e.g., from Cangqian to Tonglu), where the river is strongly influenced by freshwater runoff, the IRLS method improves the harmonic analysis results, particularly for long-period constituents. (3) The IRLS method significantly enhances the stability and accuracy of harmonic analysis results for tidal stations along the Qiantang River by effectively suppressing high-level noise and outlier interference. Therefore, the IRLS-based harmonic analysis method holds significant application value in regions with poor data quality or high background noise, such as tidal rivers.
Traditional harmonic analysis based on the ordinary least squares (OLS) method is sensitive to noise and susceptible to contamination by measurement errors and strong non-tidal processes. Harmonic analysis utilizing the iteratively reweighted least squares (IRLS) method reduces the influence of outliers by assigning them smaller weights, thereby effectively improving accuracy and stability compared to the OLS method. However, a systematic comparison of the precision of these two methods in tidal-level analysis within tidal rivers is still lacking. This study systematically compares the two methods using measured water level data from the Qiantang River in Zhejiang Province, China, through both idealized and practical experiments. The results indicate that: (1) For short time series (<3 months), the IRLS method yields more accurate results than OLS, with the mean vector difference reduced by over 2 cm, while the difference between the two methods diminishes as the time series lengthens (>3 months). (2) In the lower reaches of the Qiantang River, the difference between the two methods is minimal. However, in the middle to upper reaches (e.g., from Cangqian to Tonglu), where the river is strongly influenced by freshwater runoff, the IRLS method improves the harmonic analysis results, particularly for long-period constituents. (3) The IRLS method significantly enhances the stability and accuracy of harmonic analysis results for tidal stations along the Qiantang River by effectively suppressing high-level noise and outlier interference. Therefore, the IRLS-based harmonic analysis method holds significant application value in regions with poor data quality or high background noise, such as tidal rivers.
2025,
47(12):
165-184.
doi: 10.12284/hyxb20250105
Abstract:
Accurate long-term forecasting of ocean current fluid is crucial for marine science research, yet existing deep learning models generally suffer from error accumulation and insufficient long-term stability when processing high-dimensional spatiotemporal sequences. To address this challenge, this study proposes an innovative spatiotemporal fusion network, XLTNET. The model is based on an encoder-decoder architecture, with its core lying in the efficient fusion of two key modules: an improved Swin Transformer that adopts the K-Nearest Neighbors (KNN) sparse self-attention mechanism for precisely capturing multi-scale spatial dynamics, and an extended Long Short-Term Memory network (xLSTM) for enhancing the modeling of long-range temporal dependencies. Experiments were conducted based on the reanalysis dataset from the Copernicus Marine Service, utilizing five ocean elements including ocean current fluid (U and V components), temperature, salinity, and height. The results demonstrate that XLTNET exhibits superior performance and stability in long-term forecasting tasks. In the 15-day forecast, XLTNET was the only model to maintain an R-value above 0.7 in both flow directions. Its U-direction R-value improved by 7.3%, 18.0%, and 20.7% compared to ASTMEN, ConvLSTM, and LSTM, respectively, while its V-direction R-value showed improvements of 8.7%, 15.6%, and 17.4%. Furthermore, ablation studies confirmed the necessity of each model component and the deep fusion strategy. This research provides a high-performance solution for high-precision, long-term ocean current fluid forecasting.
Accurate long-term forecasting of ocean current fluid is crucial for marine science research, yet existing deep learning models generally suffer from error accumulation and insufficient long-term stability when processing high-dimensional spatiotemporal sequences. To address this challenge, this study proposes an innovative spatiotemporal fusion network, XLTNET. The model is based on an encoder-decoder architecture, with its core lying in the efficient fusion of two key modules: an improved Swin Transformer that adopts the K-Nearest Neighbors (KNN) sparse self-attention mechanism for precisely capturing multi-scale spatial dynamics, and an extended Long Short-Term Memory network (xLSTM) for enhancing the modeling of long-range temporal dependencies. Experiments were conducted based on the reanalysis dataset from the Copernicus Marine Service, utilizing five ocean elements including ocean current fluid (U and V components), temperature, salinity, and height. The results demonstrate that XLTNET exhibits superior performance and stability in long-term forecasting tasks. In the 15-day forecast, XLTNET was the only model to maintain an R-value above 0.7 in both flow directions. Its U-direction R-value improved by 7.3%, 18.0%, and 20.7% compared to ASTMEN, ConvLSTM, and LSTM, respectively, while its V-direction R-value showed improvements of 8.7%, 15.6%, and 17.4%. Furthermore, ablation studies confirmed the necessity of each model component and the deep fusion strategy. This research provides a high-performance solution for high-precision, long-term ocean current fluid forecasting.
2025,
47(12):
185-197.
doi: 10.12284/hyxb20250123
Abstract:
Ocean waves generally refer to wave phenomena in the ocean. Under extreme conditions, wave heights can exceed 20 meters. Waves are closely related to atmospheric motion, ocean dynamics, thermodynamic processes, and the marine environment. To address the issues of high computational load and slow speed in wave numerical models under high-resolution topography, this study utilizes MASNUM wave model data and conducts high-resolution reconstruction research for waves in the northern South China Sea based on deep learning algorithms. Through comprehensive performance evaluation of traditional linear interpolation methods and various deep learning algorithms—Convolutional Neural Networks, Generative Adversarial Networks, and diffusion models for image reconstruction—in high-resolution wave data reconstruction, results show that compared to traditional linear interpolation methods, deep learning algorithms perform better in uncovering the physical variation patterns of wave data. Furthermore, the diffusion model for image reconstruction significantly outperforms both convolutional neural networks and generative adversarial networks, achieving a comprehensive average root mean square error of merely0.0103 meters. This finding substantiates the reliability of the reconstructed high-resolution wave data and provides a novel methodological framework for establishing advanced high-resolution ocean wave data reconstruction models.
Ocean waves generally refer to wave phenomena in the ocean. Under extreme conditions, wave heights can exceed 20 meters. Waves are closely related to atmospheric motion, ocean dynamics, thermodynamic processes, and the marine environment. To address the issues of high computational load and slow speed in wave numerical models under high-resolution topography, this study utilizes MASNUM wave model data and conducts high-resolution reconstruction research for waves in the northern South China Sea based on deep learning algorithms. Through comprehensive performance evaluation of traditional linear interpolation methods and various deep learning algorithms—Convolutional Neural Networks, Generative Adversarial Networks, and diffusion models for image reconstruction—in high-resolution wave data reconstruction, results show that compared to traditional linear interpolation methods, deep learning algorithms perform better in uncovering the physical variation patterns of wave data. Furthermore, the diffusion model for image reconstruction significantly outperforms both convolutional neural networks and generative adversarial networks, achieving a comprehensive average root mean square error of merely
2025,
47(12):
198-210.
doi: 10.12284/hyxb20250117
Abstract:
Ocean surface gusts are of great significance for marine resource utilization, ocean research, and the safety of maritime transportation and engineering. However, observational methods are limited, and surface gust data remain scarce. In this study, we employ the Dual-frequency Precipitation Radar (DPR) and the GPM Microwave Imager (GMI) onboard the Global Precipitation Measurement (GPM) satellite. Brightness temperatures from GMI are used to correct Ku-band backscattering coefficients, which are then combined with ERA5 surface wind speeds to retrieve sea surface gusts, thereby enhancing gust retrieval capability. The results show that the retrieved gusts achieve a correlation coefficient (R) of 0.93 and a root mean square error (RMSE) of 1.81 m/s compared with ERA5 gusts, and R = 0.78 with RMSE = 1.88 m/s against NDBC buoy data. Retrievals from the HY-2B satellite using the same method yield R = 0.90 and RMSE = 1.84 m/s against buoy observations. Replacing ERA5 wind speeds with buoy measurements as reference further improves the retrieval accuracy of both GPM and HY-2B, highlighting the importance of accurate surface wind input. Moreover, due to its active–passive observation frequencies being more consistent with buoy observations, the GPM satellite achieves higher gust retrieval accuracy than HY-2B.
Ocean surface gusts are of great significance for marine resource utilization, ocean research, and the safety of maritime transportation and engineering. However, observational methods are limited, and surface gust data remain scarce. In this study, we employ the Dual-frequency Precipitation Radar (DPR) and the GPM Microwave Imager (GMI) onboard the Global Precipitation Measurement (GPM) satellite. Brightness temperatures from GMI are used to correct Ku-band backscattering coefficients, which are then combined with ERA5 surface wind speeds to retrieve sea surface gusts, thereby enhancing gust retrieval capability. The results show that the retrieved gusts achieve a correlation coefficient (R) of 0.93 and a root mean square error (RMSE) of 1.81 m/s compared with ERA5 gusts, and R = 0.78 with RMSE = 1.88 m/s against NDBC buoy data. Retrievals from the HY-2B satellite using the same method yield R = 0.90 and RMSE = 1.84 m/s against buoy observations. Replacing ERA5 wind speeds with buoy measurements as reference further improves the retrieval accuracy of both GPM and HY-2B, highlighting the importance of accurate surface wind input. Moreover, due to its active–passive observation frequencies being more consistent with buoy observations, the GPM satellite achieves higher gust retrieval accuracy than HY-2B.
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