2025 Vol. 47, No. 10
Display Method:
2025, 47(10): 1-11.
doi: 10.12284/hyxb20250103
Abstract:
The tropical Indo-Pacific interocean water exchange, which serves as a critical link in the oceanic thermohaline circulation and the global climate system, is one of the most hotspot oceanography and climate science. Previous observation show that the interocean exchange exhibits a distinct vertical structure, with its subsurface velocity maximum (SVM) occurring in the subsurface layer rather than the surface layer. However, existing studies have insufficient understanding of the variability of this structure as well as the underlying mechanisms. Meanwhile, the impact of SVM induced heat and freshwater transport on the Indo-Pacific climate is unclear. Based on mooring observed sea water velocity profiles, we have identified that the SVM characteristics originating from the Pacific Western Boundary Currents can propagate into the South China Sea and the Indonesian Seas, and persist along the main route of the Indonesian Throughflow from Makassar to the Lombok Strait. We propose the scientific hypothesis that the substantial freshwater and heat fluxes in the Maritime Continent, along with the propagation of equatorial planetary wave, are the key mechanisms governing the maintenance and variability of the SVM in interoceanic exchange. Here, we tried to propose an approach to investigate the SVM and its climate effect, based on observations, numerical simulations, and theoretical analyses. The research outcomes are of great significance for deepening the understanding of the interactions between the tropical Pacific and Indian Oceans, as well as air-sea interactions, and will provide theoretical support for enhancing the predictability of the Indo-Pacific climate system.
The tropical Indo-Pacific interocean water exchange, which serves as a critical link in the oceanic thermohaline circulation and the global climate system, is one of the most hotspot oceanography and climate science. Previous observation show that the interocean exchange exhibits a distinct vertical structure, with its subsurface velocity maximum (SVM) occurring in the subsurface layer rather than the surface layer. However, existing studies have insufficient understanding of the variability of this structure as well as the underlying mechanisms. Meanwhile, the impact of SVM induced heat and freshwater transport on the Indo-Pacific climate is unclear. Based on mooring observed sea water velocity profiles, we have identified that the SVM characteristics originating from the Pacific Western Boundary Currents can propagate into the South China Sea and the Indonesian Seas, and persist along the main route of the Indonesian Throughflow from Makassar to the Lombok Strait. We propose the scientific hypothesis that the substantial freshwater and heat fluxes in the Maritime Continent, along with the propagation of equatorial planetary wave, are the key mechanisms governing the maintenance and variability of the SVM in interoceanic exchange. Here, we tried to propose an approach to investigate the SVM and its climate effect, based on observations, numerical simulations, and theoretical analyses. The research outcomes are of great significance for deepening the understanding of the interactions between the tropical Pacific and Indian Oceans, as well as air-sea interactions, and will provide theoretical support for enhancing the predictability of the Indo-Pacific climate system.
2025, 47(10): 12-24.
doi: 10.12284/hyxb2025091
Abstract:
A two-dimensional numerical model of ocean internal waves was established by using the non-static governing equations of the waves without seafloor topography to study the generation, development and evolution characteristics, as well as the structure and properties of internal waves under the background flow, which is the superposition of steady vertical shear flow and the strong barotropic semi-diurnal tidal currents. The main research results are as follows: When there is an initial disturbance and only the tidal currents are used as the background flow, the disturbance does not develop. When the barotropic semi-diurnal tidal currents and the steady vertical shear flows are superimposed as the background flow, the disturbance shows shear instability, and its energy shows an increasing trend and a wave-like change in the same phase as the tidal current. After the internal waves are generated, they show movement in the direction of forward and reverse shear flow, synchronized with the phase of the tidal current. The forward movement is much faster than the reverse moving, and overall it moves along the direction of forward shear flow, indicating that the tidal current has a modulating effect on the development of internal waves. The flow function disturbance of the internal wave presents a wave packet shape composed of multiple closed positive and negative circles, with the circulation center appearing in the middle of the water body. The main body of density disturbance appears near the pycnocline and is captured by the pycnocline. Due to the interaction between tidal currents and disturbances, a single frequency harmonic wave can be transformed into a wave packet containing many frequencies, known as the frequency conversion effect of currents. This effect significantly reduces the growth rate of internal wave shear instability and has the characteristics of maintaining stability. The overall trend of the group velocity of internal waves is along the direction of shear flow. The horizontal scale of the unstable internal waves is basically independent of the initial disturbance value. The nature of these internal waves are non-equilibrium gravitational (inertial) waves.
A two-dimensional numerical model of ocean internal waves was established by using the non-static governing equations of the waves without seafloor topography to study the generation, development and evolution characteristics, as well as the structure and properties of internal waves under the background flow, which is the superposition of steady vertical shear flow and the strong barotropic semi-diurnal tidal currents. The main research results are as follows: When there is an initial disturbance and only the tidal currents are used as the background flow, the disturbance does not develop. When the barotropic semi-diurnal tidal currents and the steady vertical shear flows are superimposed as the background flow, the disturbance shows shear instability, and its energy shows an increasing trend and a wave-like change in the same phase as the tidal current. After the internal waves are generated, they show movement in the direction of forward and reverse shear flow, synchronized with the phase of the tidal current. The forward movement is much faster than the reverse moving, and overall it moves along the direction of forward shear flow, indicating that the tidal current has a modulating effect on the development of internal waves. The flow function disturbance of the internal wave presents a wave packet shape composed of multiple closed positive and negative circles, with the circulation center appearing in the middle of the water body. The main body of density disturbance appears near the pycnocline and is captured by the pycnocline. Due to the interaction between tidal currents and disturbances, a single frequency harmonic wave can be transformed into a wave packet containing many frequencies, known as the frequency conversion effect of currents. This effect significantly reduces the growth rate of internal wave shear instability and has the characteristics of maintaining stability. The overall trend of the group velocity of internal waves is along the direction of shear flow. The horizontal scale of the unstable internal waves is basically independent of the initial disturbance value. The nature of these internal waves are non-equilibrium gravitational (inertial) waves.
2025, 47(10): 25-40.
doi: 10.12284/hyxb2025097
Abstract:
Understanding regional tidal processes is of great significance for ensuring the safety of marine engineering construction and mitigating marine environmental pollution. In recent years, frequent marine development activities, particularly land reclamation projects, have been implemented in Sanmen Bay, resulting in measurable alterations to the hydrodynamic environment within the bay. Based on the three-dimensional unstructured-grid finite-volume coastal ocean model(FVCOM), a numerical model for Sanmen Bay and the adjacent waters was established. The model was validated against observed current data from three stations near the bay mouth and tidal elevation data from two stations inside the bay. Based on this validation, the tides and currents distribution characteristics as well as tidal wave propagation in Sanmen Bay and its adjacent waters were analyzed. Furthermore, by comparing the results of sensitivity experiments under the 2000 and 2020 shoreline conditions, the impacts of shoreline changes induced by reclamation on the hydrodynamic environment within the bay were quantitatively assessed. Results demonstrate that the study area exhibits predominantly semidiurnal tides, with the M2-constituent showing the largest amplitude (1.5−2 m), followed by S2, both propagating from southeast to northwest. The tidal currents within the bay are primarily rectilinear, with the maximum semi-major axis of the M2 tidal current ellipse reaching 1 m/s. Residual currents in topographically complex regions can reach 0.4 m/s. Within the bay, the residual flow enters from the northeast coast and exits toward the open sea along the southwest coast. The tidal energy flux density gradually decays during its propagation toward the bay mouth, weakening to about 20 kW/m at the entrance. Comparative analysis reveals that shoreline modifications have enhanced flood dominance within the bay, reduced M2 amplitude by 0.2 m in the bay-head region. The residual current direction has reversed from outward to inward flow along the northeastern bay mouth, while tidal energy flux density decreased by approximately 40 kW/m in some deeper channels. The numerical simulations show good agreement with field measurements, effectively reflecting recent hydrodynamic conditions in Sanmen Bay and providing scientific support for studying the impact of typical coastal reclamation on hydrodynamics.
Understanding regional tidal processes is of great significance for ensuring the safety of marine engineering construction and mitigating marine environmental pollution. In recent years, frequent marine development activities, particularly land reclamation projects, have been implemented in Sanmen Bay, resulting in measurable alterations to the hydrodynamic environment within the bay. Based on the three-dimensional unstructured-grid finite-volume coastal ocean model(FVCOM), a numerical model for Sanmen Bay and the adjacent waters was established. The model was validated against observed current data from three stations near the bay mouth and tidal elevation data from two stations inside the bay. Based on this validation, the tides and currents distribution characteristics as well as tidal wave propagation in Sanmen Bay and its adjacent waters were analyzed. Furthermore, by comparing the results of sensitivity experiments under the 2000 and 2020 shoreline conditions, the impacts of shoreline changes induced by reclamation on the hydrodynamic environment within the bay were quantitatively assessed. Results demonstrate that the study area exhibits predominantly semidiurnal tides, with the M2-constituent showing the largest amplitude (1.5−2 m), followed by S2, both propagating from southeast to northwest. The tidal currents within the bay are primarily rectilinear, with the maximum semi-major axis of the M2 tidal current ellipse reaching 1 m/s. Residual currents in topographically complex regions can reach 0.4 m/s. Within the bay, the residual flow enters from the northeast coast and exits toward the open sea along the southwest coast. The tidal energy flux density gradually decays during its propagation toward the bay mouth, weakening to about 20 kW/m at the entrance. Comparative analysis reveals that shoreline modifications have enhanced flood dominance within the bay, reduced M2 amplitude by 0.2 m in the bay-head region. The residual current direction has reversed from outward to inward flow along the northeastern bay mouth, while tidal energy flux density decreased by approximately 40 kW/m in some deeper channels. The numerical simulations show good agreement with field measurements, effectively reflecting recent hydrodynamic conditions in Sanmen Bay and providing scientific support for studying the impact of typical coastal reclamation on hydrodynamics.
2025, 47(10): 41-54.
doi: 10.12284/hyxb2025087
Abstract:
In the context of global warming, the intensity of typhoon activity has exhibited an increasing trend. Typhoons are typically associated with heavy rainfall and strong winds, which can result in significant alterations to the nearshore hydrodynamical environment over a short period, thereby triggering pronounced ecological responses. In this paper, a three-dimensional hydro-biogeochemical model was constructed based on the unstructured-grid Finite Volume Community Ocean Model (FVCOM) to study the impact of Typhoon Lekima (No. 1909) on residual currents, salinity, water quality and nutrient transport in Laizhou Bay (LZB). Sensitivity experiments were conducted to quantify the contributions of river inputs and winds to water quality during the passage of Lekima. The results show that a strong southwest coastal current developed south of the Huanghe River Estuary (HRE), and the pattern of residual currents in LZB was characterized by westward inflow and eastward outflow. The heavy rainfall led to a marked increase in freshwater and dissolved inorganic nitrogen (DIN) fluxes from the surrounding rivers into the bay. Consequently, the surface salinity near the HRE and the southwest coast of LZB decreased rapidly, while DIN miss concentrations increased. The surface salinity reached the minimum value of 25.91 two days after the passage of Lekima, which was 1.57 lower than pre-typhoon levels. Conversely, surface DIN miss concentrations peaked at 0.61 mg/L eight days after the passage of Lekima, approximately 1.51 times higher than pre-typhoon levels. Calculations of DIN fluxes through the bay mouth section revealed that the DIN exchange between LZB and the Bohai Sea (BS) occurred in two distinct phases: strong inflow and outflow during the passage of Lekima, with a total of 1.88 kt of DIN transported from LZB to the BS. The contributions of river inputs and winds to water quality were 70.15% and −18.47%, respectively. River input was identified as the primary factor of changes in water quality in LZB, while the direction of residual currents was landward due to the force of typhoon winds, which was adverse to the transport of DIN from LZB to the BS. This study underscores the crucial role of typhoons in regulating water quality changes in coastal bays and provides scientific support for sustainable development and ecological protection in coastal regions.
In the context of global warming, the intensity of typhoon activity has exhibited an increasing trend. Typhoons are typically associated with heavy rainfall and strong winds, which can result in significant alterations to the nearshore hydrodynamical environment over a short period, thereby triggering pronounced ecological responses. In this paper, a three-dimensional hydro-biogeochemical model was constructed based on the unstructured-grid Finite Volume Community Ocean Model (FVCOM) to study the impact of Typhoon Lekima (No. 1909) on residual currents, salinity, water quality and nutrient transport in Laizhou Bay (LZB). Sensitivity experiments were conducted to quantify the contributions of river inputs and winds to water quality during the passage of Lekima. The results show that a strong southwest coastal current developed south of the Huanghe River Estuary (HRE), and the pattern of residual currents in LZB was characterized by westward inflow and eastward outflow. The heavy rainfall led to a marked increase in freshwater and dissolved inorganic nitrogen (DIN) fluxes from the surrounding rivers into the bay. Consequently, the surface salinity near the HRE and the southwest coast of LZB decreased rapidly, while DIN miss concentrations increased. The surface salinity reached the minimum value of 25.91 two days after the passage of Lekima, which was 1.57 lower than pre-typhoon levels. Conversely, surface DIN miss concentrations peaked at 0.61 mg/L eight days after the passage of Lekima, approximately 1.51 times higher than pre-typhoon levels. Calculations of DIN fluxes through the bay mouth section revealed that the DIN exchange between LZB and the Bohai Sea (BS) occurred in two distinct phases: strong inflow and outflow during the passage of Lekima, with a total of 1.88 kt of DIN transported from LZB to the BS. The contributions of river inputs and winds to water quality were 70.15% and −18.47%, respectively. River input was identified as the primary factor of changes in water quality in LZB, while the direction of residual currents was landward due to the force of typhoon winds, which was adverse to the transport of DIN from LZB to the BS. This study underscores the crucial role of typhoons in regulating water quality changes in coastal bays and provides scientific support for sustainable development and ecological protection in coastal regions.
2025, 47(10): 55-68.
doi: 10.12284/hyxb2025116
Abstract:
Transparent exopolymer particles (TEP) are a special class of extracellular polymeric substances that are ubiquitous in seawater. They are characterized by their transparency, high carbon content and strong stickiness, and exhibit both colloidal and particulate characteristics. TEP play a significant role in marine carbon transportation. In this review, we describe the Bio-geochemistry characteristic and contribution to carbon transportation of transparent exopolymer particles (TEP) in the ocean systematically. Marine TEP are amorphous and highly variable in size. They are mainly produced through the self-assembly of precursors that are released by phytoplankton. TEP are removed from the ocean via biological metabolism, air-sea exchange and sedimentation. Their abundance and distribution are jointly driven by the activities of organisms such as phytoplankton and bacteria, as well as the physicochemical environment and hydrodynamic processes. TEP concentrations in coastal waters exhibit considerable variability (0−14 800 μg XG eq/L) and decrease with increasing offshore distance and are closely related to productivity, thus demonstrating marked seasonal variations. In the open ocean, TEP concentrations are usually lower (0−200 μg XG eq/L) and decrease with depth in epipelagic waters, while remaining relatively steady in mesopelagic and bathypelagic waters. As an important component of particulate organic carbon (POC), TEP typically account for less than 40% of the POC pool and its sinking flux in coastal areas, but can contribute more than 50% in the open ocean. In addition to altering particle sinking rates and affecting vertical carbon transport, TEP can also mediate air-sea carbon exchange through enrichment in the sea surface microlayer, though the underlying mechanisms and their significance in carbon transport require further investigation. Future research should focus on improving quantitative detection methods for TEP, analyzing their composition and morphology, and optimizing carbon conversion factors to better understand the biogeochemical behaviors of TEP and their mechanisms in marine carbon transportation.
Transparent exopolymer particles (TEP) are a special class of extracellular polymeric substances that are ubiquitous in seawater. They are characterized by their transparency, high carbon content and strong stickiness, and exhibit both colloidal and particulate characteristics. TEP play a significant role in marine carbon transportation. In this review, we describe the Bio-geochemistry characteristic and contribution to carbon transportation of transparent exopolymer particles (TEP) in the ocean systematically. Marine TEP are amorphous and highly variable in size. They are mainly produced through the self-assembly of precursors that are released by phytoplankton. TEP are removed from the ocean via biological metabolism, air-sea exchange and sedimentation. Their abundance and distribution are jointly driven by the activities of organisms such as phytoplankton and bacteria, as well as the physicochemical environment and hydrodynamic processes. TEP concentrations in coastal waters exhibit considerable variability (0−14 800 μg XG eq/L) and decrease with increasing offshore distance and are closely related to productivity, thus demonstrating marked seasonal variations. In the open ocean, TEP concentrations are usually lower (0−200 μg XG eq/L) and decrease with depth in epipelagic waters, while remaining relatively steady in mesopelagic and bathypelagic waters. As an important component of particulate organic carbon (POC), TEP typically account for less than 40% of the POC pool and its sinking flux in coastal areas, but can contribute more than 50% in the open ocean. In addition to altering particle sinking rates and affecting vertical carbon transport, TEP can also mediate air-sea carbon exchange through enrichment in the sea surface microlayer, though the underlying mechanisms and their significance in carbon transport require further investigation. Future research should focus on improving quantitative detection methods for TEP, analyzing their composition and morphology, and optimizing carbon conversion factors to better understand the biogeochemical behaviors of TEP and their mechanisms in marine carbon transportation.
2025, 47(10): 69-78.
doi: 10.12284/hyxb2025122
Abstract:
The influence of suspended particulate matters (SPM) on denitrifying metabolism and potential of nearshore waters were investigated in the sea area outside Dagu River estuary and southern mouth of Jiaozhou Bay based on 15N-isotopic tracing incubation experiments. Sediment cores and overlying waters were collected from two sampling sites and then incubated under in situ conditions and simulated SPM gradient (i.e., 50 mg/L, 100 mg/L, 150 mg/L, 200 mg/L, 300 mg/L and 400 mg/L in setting concentration). Detection of denitrification rates combined with relative abundances of narG and nirS genes measurement were conducted to reveal the denitrification potential. The results showed that significant denitrification occurred in all incubations. Under SPM gradient simulated condition, the denitrifying rates as well as narG and nirS gene abundances increased with SPM concentrations, and the abundance of particle-associated denitrifying bacteria probably played a dominated role on potential enhancement, implying that SPM should act as important media on promoting denitrification potential in the Jiaozhou Bay waters. The variance between two study sites was obvious with denitrification rate measured in estuary area being higher than that in southern mouth, while the functional gene abundances just the opposite. It could be well explained by the composition and grain size difference of the SPM, indicating a complicated regulation of SPM to the denitrification potential in Jiaozhou Bay waters. This study suggests that SPM will expand the spatial activity of denitrifying metabolism and then more release the denitrification potential in the nearshore ecosystem, which is ecologically meaningful for the relief of eutrophication level and risk in coastal environments.
The influence of suspended particulate matters (SPM) on denitrifying metabolism and potential of nearshore waters were investigated in the sea area outside Dagu River estuary and southern mouth of Jiaozhou Bay based on 15N-isotopic tracing incubation experiments. Sediment cores and overlying waters were collected from two sampling sites and then incubated under in situ conditions and simulated SPM gradient (i.e., 50 mg/L, 100 mg/L, 150 mg/L, 200 mg/L, 300 mg/L and 400 mg/L in setting concentration). Detection of denitrification rates combined with relative abundances of narG and nirS genes measurement were conducted to reveal the denitrification potential. The results showed that significant denitrification occurred in all incubations. Under SPM gradient simulated condition, the denitrifying rates as well as narG and nirS gene abundances increased with SPM concentrations, and the abundance of particle-associated denitrifying bacteria probably played a dominated role on potential enhancement, implying that SPM should act as important media on promoting denitrification potential in the Jiaozhou Bay waters. The variance between two study sites was obvious with denitrification rate measured in estuary area being higher than that in southern mouth, while the functional gene abundances just the opposite. It could be well explained by the composition and grain size difference of the SPM, indicating a complicated regulation of SPM to the denitrification potential in Jiaozhou Bay waters. This study suggests that SPM will expand the spatial activity of denitrifying metabolism and then more release the denitrification potential in the nearshore ecosystem, which is ecologically meaningful for the relief of eutrophication level and risk in coastal environments.
2025, 47(10): 79-89.
doi: 10.12284/hyxb2025089
Abstract:
To enhance the accuracy and reliability of extreme wave height inference in ocean engineering, this study systematically compares the applicability and uncertainty of the annual maxima (AM) method and the peak over threshold (POT) method. Utilizing reanalysis wave data from two representative sites in Hangzhou Bay, annual maxima series and POT samples were constructed. These were modeled using the generalized extreme value (GEV) distribution and the generalized Pareto distribution (GPD), respectively; for the POT method, threshold selection was optimized via a tail least squares error (TLSE) criterion. Confidence intervals for model parameters and return period level wave height estimates were further quantified using both the Delta method and the Bootstrap method. The results demonstrate that for high return periods, the POT method yields higher wave height estimates with narrower confidence intervals, rendering it more suitable for engineering design scenarios sensitive to extreme events. In uncertainty analysis, the Bootstrap method more comprehensively captures model uncertainty compared to the Delta method. This work establishes a more robust analytical framework and inferential basis for extreme wave height modeling.
To enhance the accuracy and reliability of extreme wave height inference in ocean engineering, this study systematically compares the applicability and uncertainty of the annual maxima (AM) method and the peak over threshold (POT) method. Utilizing reanalysis wave data from two representative sites in Hangzhou Bay, annual maxima series and POT samples were constructed. These were modeled using the generalized extreme value (GEV) distribution and the generalized Pareto distribution (GPD), respectively; for the POT method, threshold selection was optimized via a tail least squares error (TLSE) criterion. Confidence intervals for model parameters and return period level wave height estimates were further quantified using both the Delta method and the Bootstrap method. The results demonstrate that for high return periods, the POT method yields higher wave height estimates with narrower confidence intervals, rendering it more suitable for engineering design scenarios sensitive to extreme events. In uncertainty analysis, the Bootstrap method more comprehensively captures model uncertainty compared to the Delta method. This work establishes a more robust analytical framework and inferential basis for extreme wave height modeling.
2025, 47(10): 90-98.
doi: 10.12284/hyxb2025093
Abstract:
To ensure the structural stability of subsea infrastructure under prolonged exposure to marine dynamic loads (e.g., waves, ocean currents, and seismic activities), it is essential to understand the rheological behavior of deep-sea soft clay in the surface layer of the seabed. In this study, deep-sea soft clay was selected as the research subject, and dynamic shear tests were conducted under varying salinity and temperature conditions using a strain-controlled rheometer. The variation patterns of storage modulus (G'), loss modulus (G''), and cross-strain were systematically analyzed. By integrating liquid and plastic limit tests and free settlement tests, the influence mechanisms of salinity and temperature on the rheological properties of deep-sea soft clay were explored. The experimental results indicate that as the concentration of NaCl solution increases and temperature decreases, the liquid limit, plastic limit, settlement volume, G', G'', and cross-strain of deep-sea soft clay all exhibit an upward trend. This behavior is closely associated with the formation of a flocculated clay structure and the reduction in thickness of the double electric layer. Under increasing shear strain, deep-sea soft clay demonstrates a distinct two-step yielding behavior: the first yield occurs during the initial stage of modulus reduction, corresponding to the breakdown of the flocculated network; the second yield appears in the subsequent modulus reduction phase, associated with the disruption of the shear-induced hollow cylindrical structure. The plateau phase between the two yielding stages reflects the shear resistance provided by the hollow cylindrical structure. The findings of this study provide a scientific foundation for the design and stability evaluation of engineering foundations in ultra-deep marine environments.
To ensure the structural stability of subsea infrastructure under prolonged exposure to marine dynamic loads (e.g., waves, ocean currents, and seismic activities), it is essential to understand the rheological behavior of deep-sea soft clay in the surface layer of the seabed. In this study, deep-sea soft clay was selected as the research subject, and dynamic shear tests were conducted under varying salinity and temperature conditions using a strain-controlled rheometer. The variation patterns of storage modulus (G'), loss modulus (G''), and cross-strain were systematically analyzed. By integrating liquid and plastic limit tests and free settlement tests, the influence mechanisms of salinity and temperature on the rheological properties of deep-sea soft clay were explored. The experimental results indicate that as the concentration of NaCl solution increases and temperature decreases, the liquid limit, plastic limit, settlement volume, G', G'', and cross-strain of deep-sea soft clay all exhibit an upward trend. This behavior is closely associated with the formation of a flocculated clay structure and the reduction in thickness of the double electric layer. Under increasing shear strain, deep-sea soft clay demonstrates a distinct two-step yielding behavior: the first yield occurs during the initial stage of modulus reduction, corresponding to the breakdown of the flocculated network; the second yield appears in the subsequent modulus reduction phase, associated with the disruption of the shear-induced hollow cylindrical structure. The plateau phase between the two yielding stages reflects the shear resistance provided by the hollow cylindrical structure. The findings of this study provide a scientific foundation for the design and stability evaluation of engineering foundations in ultra-deep marine environments.
2025, 47(10): 99-110.
doi: 10.12284/hyxb2025085
Abstract:
Mesoscale eddies, as an important phenomenon in the ocean, significantly influence the distribution of water masses and material transport within their regions. Obtaining the three-dimensional distribution of mesoscale eddies is of great significance for marine resource development, maritime transportation, and military applications. However, existing intelligent identification models for mesoscale eddies typically rely on sea surface data such as sea surface height and sea surface temperature, and are only used to identify mesoscale eddies at the ocean surface. This paper proposes a multi-scale feature adaptive fusion model based on multi-source data, including flow fields, temperature, and salinity. In the encoder stage, the model uses a multi-branch structure to independently extract features from the multi-source data. In the decoder stage, an attention mechanism is employed to perform weighted adaptive fusion of multi-layer features from each branch. During training, a hybrid loss function combining classification probability gradient loss and Dice coefficient loss is used to improve the identification accuracy of the model. Experimental validation is conducted using data from the South China Sea region. The model achieves a global accuracy of 98.49%, an average Dice coefficient of0.8777 , and a weighted Dice coefficient of 0.8225 , demonstrating the model’s effectiveness and high accuracy in identifying the distribution of mesoscale eddies at both the sea surface and various water depths.
Mesoscale eddies, as an important phenomenon in the ocean, significantly influence the distribution of water masses and material transport within their regions. Obtaining the three-dimensional distribution of mesoscale eddies is of great significance for marine resource development, maritime transportation, and military applications. However, existing intelligent identification models for mesoscale eddies typically rely on sea surface data such as sea surface height and sea surface temperature, and are only used to identify mesoscale eddies at the ocean surface. This paper proposes a multi-scale feature adaptive fusion model based on multi-source data, including flow fields, temperature, and salinity. In the encoder stage, the model uses a multi-branch structure to independently extract features from the multi-source data. In the decoder stage, an attention mechanism is employed to perform weighted adaptive fusion of multi-layer features from each branch. During training, a hybrid loss function combining classification probability gradient loss and Dice coefficient loss is used to improve the identification accuracy of the model. Experimental validation is conducted using data from the South China Sea region. The model achieves a global accuracy of 98.49%, an average Dice coefficient of
2025, 47(10): 111-125.
doi: 10.12284/hyxb2025095
Abstract:
To address the problem of low prediction accuracy for Arctic sea ice concentration during the melting season, this study proposes a method for predicting Arctic sea ice concentration based on an improved SA-ConvLSTM model, enabling two-dimensional spatiotemporal prediction of monthly mean sea ice concentration data for the coming year. The method uses the SA-ConvLSTM model as the core unit, incorporating a Seq2Seq prediction structure and a VGG16-like encoder-decoder architecture to specifically address the uncertainty in selecting the output step length of time series predictions. In addition, a composite loss function is designed to optimize the training process, further enhancing the spatiotemporal prediction accuracy of sea ice concentration distribution. Using the Arctic Ocean as the study area, and based on monthly climate sea ice concentration data jointly released by the National Snow and Ice Data Center (NSIDC) and the National Oceanic and Atmospheric Administration (NOAA), the model predicts the spatiotemporal distribution of Arctic sea ice concentration in 2023 and compares the results with real observations. The results show that, compared with traditional LSTM, ConvLSTM, and the unmodified SA-ConvLSTM models, the improved model achieves significant advantages in all evaluation metrics: root mean square error decreases by 13.18%, 36.10%, and 22.58%; correlation coefficient increases by 1.90%, 5.97%, and 3.31%; structural similarity index improves by 5.38%, 15.00%, and 10.30%; and sea ice area error decreases by 83.46%, 76.53%, and 60.30%, respectively. Furthermore, analysis of predictions for the extreme melting years of 2012 and 2020 further verifies the model’s stability and robustness under abnormal climatic conditions, demonstrating strong adaptability and practical application potential. The proposed spatiotemporal prediction model can more accurately predict the spatial distribution of sea ice during the melting season and effectively capture complex spatiotemporal variations and fine-scale details.
To address the problem of low prediction accuracy for Arctic sea ice concentration during the melting season, this study proposes a method for predicting Arctic sea ice concentration based on an improved SA-ConvLSTM model, enabling two-dimensional spatiotemporal prediction of monthly mean sea ice concentration data for the coming year. The method uses the SA-ConvLSTM model as the core unit, incorporating a Seq2Seq prediction structure and a VGG16-like encoder-decoder architecture to specifically address the uncertainty in selecting the output step length of time series predictions. In addition, a composite loss function is designed to optimize the training process, further enhancing the spatiotemporal prediction accuracy of sea ice concentration distribution. Using the Arctic Ocean as the study area, and based on monthly climate sea ice concentration data jointly released by the National Snow and Ice Data Center (NSIDC) and the National Oceanic and Atmospheric Administration (NOAA), the model predicts the spatiotemporal distribution of Arctic sea ice concentration in 2023 and compares the results with real observations. The results show that, compared with traditional LSTM, ConvLSTM, and the unmodified SA-ConvLSTM models, the improved model achieves significant advantages in all evaluation metrics: root mean square error decreases by 13.18%, 36.10%, and 22.58%; correlation coefficient increases by 1.90%, 5.97%, and 3.31%; structural similarity index improves by 5.38%, 15.00%, and 10.30%; and sea ice area error decreases by 83.46%, 76.53%, and 60.30%, respectively. Furthermore, analysis of predictions for the extreme melting years of 2012 and 2020 further verifies the model’s stability and robustness under abnormal climatic conditions, demonstrating strong adaptability and practical application potential. The proposed spatiotemporal prediction model can more accurately predict the spatial distribution of sea ice during the melting season and effectively capture complex spatiotemporal variations and fine-scale details.
2025, 47(10): 126-136.
doi: 10.12284/hyxb20250101
Abstract:
The Madden-Julian Oscillation (MJO), as the primary mode of tropical intraseasonal variability, plays a critical role in improving subseasonal prediction skill. However, due to its multi-scale evolutionary characteristics and highly nonlinear dynamical processes, existing prediction methods still struggle to effectively capture the complex spatiotemporal structure of MJO. To address this issue, we propose a novel prediction model named MISM (Multi-modal data and Integrated Spatiotemporal features for MJO prediction), which integrates multimodal inputs and spatiotemporal feature extraction. The model jointly leverages historical Real-time Multivariate MJO (RMM) indices and multiple meteorological variables as inputs. It constructs a spatial feature extraction module based on Squeeze-and-Excitation (SE) blocks, convolutional layers, and the Swin Transformer, as well as an autoregressive attention mechanism for nonlinear temporal modeling. Experimental results demonstrate that the MISM model extends predictive skill to beyond 30 d and shows overall superior performance compared with traditional dynamical and statistical methods in long-lead forecasts beyond 25 d. Furthermore, saliency maps are utilized to analyze the contribution regions of meteorological factors. The results reveal that the western Pacific and the Indonesian archipelago consistently exhibit high sensitivity across different lead times, with oceanic regions generally contributing more than land areas. Water vapor and sea surface temperature anomalies play a more prominent role in short- to medium-term forecasts, while low-level wind fields and convective activity contribute more significantly in longer-term forecasts. High-level circulation exerts a stable influence across all lead times, highlighting the model’s ability to capture the mechanisms of MJO evolution.
The Madden-Julian Oscillation (MJO), as the primary mode of tropical intraseasonal variability, plays a critical role in improving subseasonal prediction skill. However, due to its multi-scale evolutionary characteristics and highly nonlinear dynamical processes, existing prediction methods still struggle to effectively capture the complex spatiotemporal structure of MJO. To address this issue, we propose a novel prediction model named MISM (Multi-modal data and Integrated Spatiotemporal features for MJO prediction), which integrates multimodal inputs and spatiotemporal feature extraction. The model jointly leverages historical Real-time Multivariate MJO (RMM) indices and multiple meteorological variables as inputs. It constructs a spatial feature extraction module based on Squeeze-and-Excitation (SE) blocks, convolutional layers, and the Swin Transformer, as well as an autoregressive attention mechanism for nonlinear temporal modeling. Experimental results demonstrate that the MISM model extends predictive skill to beyond 30 d and shows overall superior performance compared with traditional dynamical and statistical methods in long-lead forecasts beyond 25 d. Furthermore, saliency maps are utilized to analyze the contribution regions of meteorological factors. The results reveal that the western Pacific and the Indonesian archipelago consistently exhibit high sensitivity across different lead times, with oceanic regions generally contributing more than land areas. Water vapor and sea surface temperature anomalies play a more prominent role in short- to medium-term forecasts, while low-level wind fields and convective activity contribute more significantly in longer-term forecasts. High-level circulation exerts a stable influence across all lead times, highlighting the model’s ability to capture the mechanisms of MJO evolution.
2025, 47(10): 137-145.
doi: 10.12284/hyxb2025099
Abstract:
Terrain measurement data in shallow water areas provide essential support for marine resource development and water resource investigation and management, making them a focal point in marine surveying and related fields. Airborne LiDAR Bathymetry (ALB) is a high-precision, high-efficiency, and highly mobile measurement technology particularly suited for topographic surveys in shallow water regions. To address the inefficiencies and high costs associated with traditional on-site sampling methods ( Secchi disk transparency measurements) for estimating maximum bathymetric depth using airborne LiDAR, this study proposes a novel method for maximum depth estimation without on-site sampling, integrating satellite remote sensing water color data products with waveform modeling. By retrieving the diffuse attenuation coefficient at 532 nm through inversion of NASA Ocean Color’s Kd(490) product and combining it with a physical model of LiDAR bathymetric echo signals, the method constructs a superimposed waveform model incorporating contributions from the water surface, water column, seabed, and noise. A peak detection algorithm is then employed to automate maximum depth determination. Experimental results from Jiaozhou Bay in Qingdao City, Shandong Province, and Tuosu Lake in Delingha City, Qinghai Province, demonstrate that the proposed method achieves maximum depth estimation with deviations not exceeding 0.4 m and relative errors within 5%, validating its effectiveness. By replacing on-site transparency measurements with satellite remote sensing water color data products, the proposed method eliminates the need for field sampling, significantly reducing the operational costs of airborne LiDAR bathymetry while providing efficient technical support for shallow water mapping and water resource investigations.
Terrain measurement data in shallow water areas provide essential support for marine resource development and water resource investigation and management, making them a focal point in marine surveying and related fields. Airborne LiDAR Bathymetry (ALB) is a high-precision, high-efficiency, and highly mobile measurement technology particularly suited for topographic surveys in shallow water regions. To address the inefficiencies and high costs associated with traditional on-site sampling methods ( Secchi disk transparency measurements) for estimating maximum bathymetric depth using airborne LiDAR, this study proposes a novel method for maximum depth estimation without on-site sampling, integrating satellite remote sensing water color data products with waveform modeling. By retrieving the diffuse attenuation coefficient at 532 nm through inversion of NASA Ocean Color’s Kd(490) product and combining it with a physical model of LiDAR bathymetric echo signals, the method constructs a superimposed waveform model incorporating contributions from the water surface, water column, seabed, and noise. A peak detection algorithm is then employed to automate maximum depth determination. Experimental results from Jiaozhou Bay in Qingdao City, Shandong Province, and Tuosu Lake in Delingha City, Qinghai Province, demonstrate that the proposed method achieves maximum depth estimation with deviations not exceeding 0.4 m and relative errors within 5%, validating its effectiveness. By replacing on-site transparency measurements with satellite remote sensing water color data products, the proposed method eliminates the need for field sampling, significantly reducing the operational costs of airborne LiDAR bathymetry while providing efficient technical support for shallow water mapping and water resource investigations.
2025, 47(10): 146-154.
doi: 10.12284/hyxb2025083
Abstract:
Ocean buoy observations serve as a vital means of acquiring data for marine research. However, direct measurements from buoys are subject to significant biases induced by factors such as sensor baseline drift, biofouling, and seawater corrosion, necessitating rigorous bias correction to ensure data reliability. While numerous quality control (QC) schemes for physical oceanographic parameters from buoy data have been extensively studied and reported, robust and practical sensor QC measures for more complex and variable chemical parameters remain lacking. To address this gap, this study analyzed 90-day laboratory monitoring data for parameters including dissolved oxygen, chlorophyll concentration, pH, and partial pressure of CO2 (pCO2). The analysis revealed that the drift bias in these monitored parameters exhibits a strong linear correlation with fundamental sensor parameters such as conductivity and sensor output voltage and with biological factors to varying degrees. Building upon these findings, we developed a drift bias correction method based on machine learning algorithm to fit the nonlinear relationships between drift bias and fundamental sensor parameters. This method effectively validates buoy sensor data for chemical parameters. Application of this method to observational data across different parameters significantly reduces the deviation between drifted data and true values. It thus provides a novel QC approach for achieving sustained, stable, and high-quality acquisition of marine chemical parameter data from buoy observations.
Ocean buoy observations serve as a vital means of acquiring data for marine research. However, direct measurements from buoys are subject to significant biases induced by factors such as sensor baseline drift, biofouling, and seawater corrosion, necessitating rigorous bias correction to ensure data reliability. While numerous quality control (QC) schemes for physical oceanographic parameters from buoy data have been extensively studied and reported, robust and practical sensor QC measures for more complex and variable chemical parameters remain lacking. To address this gap, this study analyzed 90-day laboratory monitoring data for parameters including dissolved oxygen, chlorophyll concentration, pH, and partial pressure of CO2 (pCO2). The analysis revealed that the drift bias in these monitored parameters exhibits a strong linear correlation with fundamental sensor parameters such as conductivity and sensor output voltage and with biological factors to varying degrees. Building upon these findings, we developed a drift bias correction method based on machine learning algorithm to fit the nonlinear relationships between drift bias and fundamental sensor parameters. This method effectively validates buoy sensor data for chemical parameters. Application of this method to observational data across different parameters significantly reduces the deviation between drifted data and true values. It thus provides a novel QC approach for achieving sustained, stable, and high-quality acquisition of marine chemical parameter data from buoy observations.
Email alert
RSS