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Marine gas hydrate deposits are significant temporal reservoirs for hydrocarbons migrating from deep sources. This is crucial to our understanding of ocean carbon cycling. The cold seep, a geological process regarding gas leakage from deep or shallow sources, is usually linked with gas hydrate decomposition. In this thesis, we reviewed the latest applications of in situ monitoring and detecting methods regarding the leakage plumes, migration pathways, and seafloor geomorphologies associated with gas hydrate and cold seep systems, primarily including vessel-and land-based gas plume measurements, surface ocean-lower atmosphere hydrocarbon emission detections, seafloor visualization techniques, and in situ observation networks. The integrated applications of these in situ observation methods provide a nuanced view of the temporal and spatial variability of hydrate and cold seep systems, facilitate understanding of the fate of hydrocarbons, and expand our knowledge of cold-seep biota in a watery desert.

The monitoring of carbon flux dynamics and assessment of carbon sink functions of island forest ecosystems are rarely reported due to their special geographical location and few data sources. In this study, the forest ecosystem of the Nanji Island was used as the research object, the carbon sink potential of island forests and their driving factors were assessed. Based on eddy correlation techniques, the temporal variation characteristics and driving factors of net ecosystem productivity (NEP), gross primary productivity (GPP), and ecosystem respiration (Reco) from 2020 to 2021 were explored. Results showed that the forest ecosystem of Nanji Island was carbon sink. Net CO₂ uptake in 2020 and 2021 were 516 g C·m^-2·a^-1 and 598 g C·m^-2·a^-1, Reco were 1 037 g C·m^-2·a^-1 and 1 646 g C·m^-2·a^-1, and GPP were 1 552 g C·m^-2·a^-1 and 2 244 g C·m^-2·a^-1, respectively. Total solar radiation (Rg), photosynthetically active radiation (PAR), net radiation (Rn) and sensible heat (H) were significantly and positively correlated with NEP and GPP (p≤0.001); air temperature (Tair) and soil temperature (Tsoil) were significantly and positively correlated with Reco(p≤0.001). The photosynthesis time of Nanji Island forest was longer than the carbon sink time on the daily scale. When Tair reached 10.05-27.76 ℃ and PAR reached 110.47-429.44 μmol·m^-2·s^-1, the photosynthesis intensity of island forest was higher than that of ecosystem respiration, which showed CO₂ absorption. The monitoring and assessment of carbon fluxes in the forest ecosystems of Nanji Island will provide an important theoretical support for the establishment of a dynamic monitoring and assessment management system for blue carbon in China.

Artificial intelligence in oceanography has demonstrated a great potential with the explosive growth of ocean observation data and numerical model products. This article first reviews the history of ocean big data development, and then introduces in detail the current status of artificial intelligence in oceanography applications including identifying ocean phenomenon, forecasting ocean variables and phenomenon, estimating dynamic parameters, correcting forecast errors, and solving dynamic equations. Specifically, this article elaborates the research on the intelligent identification of ocean eddies, internal waves and sea ice, the intelligent prediction of sea surface temperatures, El Ni?o-Southern Oscillation, storm surges, waves and currents, the intelligent estimation of ocean turbulence parameterization for numerical models, and the intelligent correction of waves and current forecast errors. In addition, it discusses the recent progress of applying physical mechanism fusion and Fourier neural operator for solving ocean dynamic equations. This article is based on the current status of artificial intelligence in oceanography and aims to provide a comprehensive demonstration of the advantages and potential of applying artificial intelligence methods in the field of oceanography. With the two emerging research hotspots: digital twin oceans and artificial intelligence large models, the future development direction of artificial intelligence provides enlightenment and reference for interested scientists and researchers.

With the advancement of observation technology and the improvement of ocean numerical simulation capabilities, some stable subsurface coherent vortices have been widely observed in the ocean, which far from the formation source area. These vortices possess distinctive dynamic characteristics, such as a low potential vorticity center, lens-like structure of isopycnals, weak stratification, and anomalous temperature, salinity, or other tracer properties compared to the surrounding water mass. Their core flow is relatively stronger. These subsurface coherent vortices significantly impact ocean water mass transport, thermohaline circulation and marine biological environment. This paper comprehensively summarizes researches on subsurface coherent vortices in the ocean, including their structure, hydrological characteristics, identifying methods, global distribution, dynamic mechanisms and their important effects on ocean environment. Furthermore, the research perspectives are discussed, such as the difficulties in the research and the issues that need to be solved to comprehensively understand subsurface coherent vortices in the ocean.

Wave is an important factor to shape the dynamic geomorphology of the open tidal flat, but researches on tidal-flat wave characteristics are still limited. Taking Nanhui tidal flats in the Changjiang Estuary as an example, the wave characteristic parameters and wave spectrum parameters were obtained by inverting flow-velocity and water-pressure data from the Acoustic Doppler Velocimeters (ADVs) at some fixed platforms, and their changes over tidal cycles and associated influence mechanisms were discussed. The results show that both normal wave direction and prominent wave direction at three stations of Nanhui tidal flats are mainly southeast during the observation period, with long-period swells dominating. The effective wave height of the three stations is positively correlated with the water depth, but the fitting coefficients of each station are different over flood and ebb periods. Wave orbital velocities are obviously modulated by the shallow water effect and the flow directions, and their maximum values usually occur at the early flooding stage, while minimum values can be observed to occur during the current transition periods. The wave energy spectrum during ebb tides is featured by the bimodal pattern because of high influence by tidal levels and coastal topography, and the peak energy is continuously attenuated and gradually dispersed with the concurrent shift of peak frequencies.

Mangroves, coastal salt marshes and seagrass beds, as the typical coastal blue carbon ecosystems, have been widely recognized for their remarkable capacity in carbon storage. Vegetation carbon pool and sediment (or soil) carbon pool were considered to be the major carbon pools within the coastal blue ecosystems and their variations determined the overall carbon sequestration of the ecosystems. From a perspective of carbon pool interactions, this study summarized the previous research work based on literature review, including the interactions within various vegetation carbon pools and within various sediment carbon pools, as well as the interactions between vegetation and sediment carbon pools. Interspecific competition, allochthonous carbon input and biogeomorphology were found to be the key to understand the carbon pool interactions. Finally, a perspective on the current state-of-the-art of blue carbon pool study is offered, with challenges and suggestions for future directions.

Aiming at the problems of difficulty in verifying the accuracy of the interpolation model of single-beam bathymetric data, a method based on high-precision UAV data to verify the accuracy of the interpolation model was proposed by using the intertidal tidal law. At low tide, UAV photogrammetry was used to construct a high-precision digital surface model (DSM) of the intertidal zone, and at high tide, the single-beam bathymetry data was obtained and the three-dimensional coordinates of the intertidal topographic points were calculated by combining the global navigation satellite system (GNSS) technology, and constructed an intertidal digital elevation model (DEM) by using the following 4 interpolation methods: Kriging, inverse distance weight, completely regularized spline and natural neighborhood interpolation method. Based on UAV data, the accuracy analysis of intertidal zone DEM was carried out. The results show that: (1) PPK technology-assisted UAV photogrammetry can construct high-precision intertidal zone DSM. (2) In the intertidal zone, UAV data can be used as an evaluation criterion for the accuracy of single-beam bathymetry data. (3) When the seabed topography is relatively flat, the completely regularized spline method has higher accuracy than the other three methods, and the coarse difference rate is 12.5%.

Abyssal peridotite is widely distributed in tectonic environments such as mid-ocean ridges, subduction zones, and continental margins, and typically undergoes subsequent alterations, among which serpentinization is the most significant type. Serpentinization refers to the chemical process wherein ferromagnesium-rich minerals in peridotite, such as olivine and pyroxene, are replaced by a series of secondary minerals like serpentine, magnetite, and brucite. The conditions of serpentinization are closely linked with hydrothermal circulation and the migration of mineral-forming substances, bearing significant implications for indicating hydrothermal mineralization. Traditional methods of petrology and geochemistry exhibit polysemic interpretations and uncertainties when reflecting serpentinization conditions, with different minerals or chemical indicators possibly suggesting different outcomes. Oxygen isotopes are ubiquitous in nature and the oxygen isotope tracing method, due to its wide applicability, ease of comparison, and support for in-situ micro-zone analysis, can clearly reflect the reaction conditions and processes of the mineral or rock-fluid system. This study primarily provides an overview of the principles of oxygen isotope thermometry, the process of abyssal peridotite serpentinization, application cases of oxygen isotope thermometry in the serpentinization of abyssal peridotite, factors influencing the oxygen isotope compositions of serpentinites, as well as the advantages and limitations of oxygen isotope thermometry. It aims to offer a reference for a more profound understanding of the serpentinization process of abyssal peridotite.

In order to explore the distribution, formation mechanism and motion law of eddy around Kuroshio in East China Sea, firstly, the eddy distribution around the Kuroshio in the East China Sea during the past 27 years was analyzed by using the mesoscale eddy data set of the AVISO(Archiving, Validation and Interpretation of Satellite Oceanographic Data). The results show that there are 650 eddies generated in the meander of the Kuroshio, and 271 eddies in the middle part of the Kuroshio. The diameters of the most these eddies were between 100 to 150 km, and the amplitudes were between 2 to 6 cm. Secondly, the motion path and eddy motion process of the Kuroshio in the East China Sea are also analyzed. The results show these the cyclonic eddies are easy to be generated in the inner side of the Kuroshio cyclonic bend, with a long path. For example, at the cyclone bend of the Kuroshio axis in the northeast of Taiwan, the average length of the path was 87.6 km. Otherwise, when these anticyclonic eddies are generated, these eddies usually are wandered. In the middle part of the Kuroshio, the eddy showed the polar symmetric distribution characteristics of these cyclonic eddies in the west side of the Kuroshio main axis and these anticyclonic eddies in the east side of the Kuroshio main axis. Both types of eddies moved northeastward along the Kuroshio main axis. Finally, combined with reanalysis data sets of ocean current and sea surface height, the eddy motion law and generation mechanism were discussed. It is concluded that these eddies generation at the curve of the Kuroshio are related to the separation of the boundary layer of the Kuroshio fluid. The Kuroshio countercurrent from the south of Yanmei Island to the west of Okinawa Island played a key role in the polar symmetric distribution of these eddies in the middle part of the Kuroshio. These eddies usually experience three stages of growth, maturation and decay in the process of movement.

Nitrate is the main nitrogen form available for phytoplankton life activities in the ocean, and its nitracline depth (Z_N) directly affects the vertical transport of nitrate and the ocean primary productivity, and then further influences the carbon cycle. With the advancement of ocean observation technologies, the profile data of nitrate have been collected in diversified ways, such as ship-based CTD observations and BGC-Argo automatic observations. The vertical sampling resolution of these techniques varies significantly (the vertical sampling resolution of CTD is lower than that of BGC-Argo). In view of different sampling data, it is urgent to conduct systematic and quantitative comparative analysis and study on the computing methods of Z_N. In this study, three different methods: difference method, gradient method and threshold method, are adopted to compute the corresponding Z_N by using the historical ship-based CTD data and BGC-Argo buoy data in the Northwest Pacific Ocean. The results show that in the case of single nitrate profile, based on BGC-Argo data, the difference between observed Z_N and the Z_N calculated by difference method is only 0.2 m, followed by threshold method is 20.0 m and gradient method is 202.8 m at most. Based on CTD data, the difference between observed Z_N and Z_N calculated by difference method is 2.0 m, the threshold method is 49.0 m, and the gradient method is 155.0 m. Compared with the gradient method and threshold method, the difference between the Z_N calculated by the difference method and the observed Z_N is the smallest. According to the results of statistical error analysis, it is found that the Z_N calculated by the three methods based on BGC-Argo data show a good correlation with the observed Z_N. Among them, the error of difference method is the smallest (R²=0.77, RMSE=28.48 m). The R² and RMSE of threshold method are 0.64 and 34.85 m, and the R² and RMSE of gradient method are 0.52 and 53.80 m. For CTD data, due to its low vertical sampling resolution, the Z_N calculated by the three methods is quite different from the observed Z_N. However, compared with the gradient method and threshold method, the error of the difference method is still the smallest (R²=0.81, RMSE =16.13 m). The R² and RMSE of threshold method are 0.47 and 27.65 m, and the R² and RMSE of gradient method are 0.42 and 36.41 m. The applicability of each method is preliminarily explored through comparing and analyzing the characteristics and differences of them so as to provide some scientific reference for the in-depth research on the vertical distribution characteristics and upward transport process of nitrate.

The Indian Ocean Dipole (IOD) is a pivotal climate phenomenon in the Indian Ocean region, exerting a significant impact on the climate change of the surrounding areas and the global climate system. Accurate prediction of IOD is essential for comprehending the dynamics of the global climate, yet traditional forecasting methods are limited in capturing its complexity and nonlinearity, constraining predictive capabilities. This paper begins by outlining the relevant theories of IOD and evaluates the strengths and weaknesses of traditional forecasting methods. It then provides a comprehensive analysis of the application and development of deep learning in the field of IOD prediction, highlights the advantages of deep learning models over traditional methods in terms of automatic feature extraction, nonlinear relationship modeling, and large data processing capabilities. Additionally, the paper discusses the challenges faced by deep learning models in IOD forecasting: including data scarcity, overfitting, and model interpretability issues, and proposes future research directions to promote innovation and progress in the application of deep learning technology in the field of climate prediction.

Two-phase leaching method can separate hydrogenetic and residual mineral phases of ferromanganese nodules, which will provide valuable information with the research of ferromanganese nodule mineralization and pale-environment. In order to study the applicability of the two-phase leaching method in the nodule mineralization research for different types of nodules compared to the bulk sample results, the ferromanganese nodules samples from six stations were selected, which were collected on the seamounts area of western Pacific Ocean and CC Zone of eastern Pacific Ocean, respectively. Geochemical and mineralogical compositions of the bulk samples were analyzed, and elements compositions of the hydrogenetic and residual mineral phases were analyzed, which were obtained using the two-phase leaching method. The results indicated that the mass ration variation of the residual mineral phases was not obvious with about 14.0%-17.6%, which had high contents of Nb, Rb, Ta, Ti, Zr. Contents and relevant rations of elements such as Co, Ni, Cu, Zn, Sr, REY in the hydrogenetic mineral phase showed similar features with those in the bulk samples. Proportion of elements contents like Ti, Nb, Sr between hydrogenetic and residual mineral phases showed well negative correlation with Mn/Fe values, which probably could be regarded as index for ferromanganese nodule mineralized environment research in the future.

The multiple indices of n-alkanes including ∑oddC_25-33, CPI, ACL, C₃₁/C₂₇ and P_mar-aq from a sediment core HKUV16 retrieved in the Pearl River Estuary were analyzed to explore their sources and environmental changes since the mid-Holocene. The distribution features of n-alkanes of core HKUV16 indicated that they were mainly from terrestrial higher vegetation. From 8.0 to 7.0 ka BP, ∑oddC_25-33, CPI, and P_mar-aq increased, while ACL and C₃₁/C₂₇ were low, which showed that terrestrial organic matter input increased and woody vegetation increased. The decrease of ∑oddC_25-33 during 7.0~3.2 ka BP indicated that the input of terrestrial organic matter decreased, while the ACL and C₃₁/C₂₇ showed an increase-decrease-increase trend, suggesting that the Pearl River Basin might have experienced dry-wet-dry climate change. From 3.2 to 2.2 ka BP, high ACL and C₃₁/C₂₇ indicated that herbaceous plants expanded and the climate was relatively arid. The multi-indices showed that the input of n-alkanes to the Pearl River Estuary before 2.2 ka BP was mainly affected by East Asian summer monsoon. However, increasing human activities since 2.2 ka BP might have become the dominant factor for the ecological environment of the Pearl River Basin.

The variation characteristics of water and sediment in the Pearl River Basin over the past about 70 years were analyzed by using the runoff and sediment discharge data at three major hydrological stations in the Pearl River Basin, Gaoyao Station (Xijiang River), Shijiao Station (Beijiang River) and Boluo Station (Dongjiang River) from 1954 to 2019, and applying the Mann-Kendall nonparametric test and accumulative anomaly curves. The results showed that the change of the runoff in the Pearl River from 1954 to 2019 was not obvious while the sediment discharge was decrease, among which the sediment discharge at Gaoyao and Boluo Stations changed abruptly in 2003 and 1991, decreasing by 71.3% and 48.88%, respectively, compared with before the abrupt change, while the sediment discharge at Shijiao Station did not change abruptly. On this basis, the change of vegetation cover in the basin and its relationship with the change of water and sediment were studied by using the precipitation data and Normalized Difference Vegetation Index (NDVI) in the basin from 2000 to 2019. The NDVI in the Pearl River Basin showed an overall increasing trend from 2000 to 2019, and the improvement area of vegetations cover accounted for 89.27% of the total basin area. Among which NDVI in the Pearl River Basin was poorly correlated with sediment discharge from 2000 to 2008, NDVI in the Xijiang River Basin and Dongjiang River Basin were positively correlated with precipitation in the basin but not with sediment discharge form 2009 to 2019, and only in the Beijiang River Basin, NDVI was insignificantly negatively correlated with sediment discharge. The influence of human activities on sediment discharge in the Pearl River at different periods was discussed using the double cumulative curve method. Since the 1980s, human activities such as deforestation, reservoir construction, and soil and water conservation had a significant impact on sediment discharge. After 2000, the construction of Longtan Reservoir was the main reason for the decline of sediment discharge in the Xijiang River, but the improvement of vegetation coverage had no significant impact on the sediment discharge in the Pearl River Basin.

Combined with the Holland wind fields and the ERA5 wind fields, the mixed wind fields was set up by introducing a weight coefficient varying with the radius of wind speed, and a typhoon wave model in Zhejiang sea area was established using MIKE21 SW. Then, the Holland, the ERA5 and the mixed wind fields were used as the input wind fields to simulate the wind speed and the significant wave height during No.1918 typhoon Mitag, respectively. The verification shows that the simulated results obtained using the Holland wind fields and the ERA5 wind fields cannot agree accurately with the observed data, while the mixed wind fields proposed in this study can improve the simulation accuracy. In order to study whether the above conclusion is universal in Zhejiang sea area, five typical typhoons that have the most serious impact on Zhejiang sea area in the recent 5 years were selected for typhoon wave numerical simulations and the error statistical analysis. The results indicate the wind speed around the typhoon center is relatively good using the Holland wind fields and the average relative errors of the maximum wind speed are 8.62%-10.19%, but the average relative errors of the wind speed below 10 m/s is relatively bigger, reaching 29.76%-44.29%. However, the wind speed around the typhoon center using the ERA5 wind fields is smaller than the observed data, and the average relative errors of the maximum wind speed are 17.64%-25.77%, but the average relative errors of wind speed below 10 m/s are smaller than that using the Holland wind fields, which are 19.64%-32.00%. During the five typhoon processes, the average values of the average relative errors of the significant wave height driven by Holland, the ERA5 and the mixed wind fields are 29.92%, 25.62% and 22.82%, respectively. Correspondingly, the average root mean square errors are 0.46 m, 0.42 m and 0.39 m and the consistency indexes are 0.94, 0.95 and 0.96. The above results shows that the mixed wind fields proposed in this study is universal in Zhejiang sea area and can improve the simulation accuracy of typhoon waves.

Histopathological indexes can be used to assess health of organism, but their application faces challenges such as low efficiency, high cost and strong subjectivity. Introducing artificial intelligence (AI) technology into histopathological analysis of biological tissues can leverage its high-throughput image analysis capabilities, overcoming the limitations in assessing and monitoring marine organism health. Based on our review on health assessment indicators of marine organisms, the application of AI in image analysis, and the use of AI for histopathological image processing, a deep learning-based histopathological image analysis approach was proposed using gill tissues of marine mussels as representative. Through a series of processes such as training, validation, and prediction of histopathology images, it was determined that the Res-UNet deep learning model can efficiently and accurately quantify histopathological damage in mussels’ gills. An automated, high-throughput, and less subjective workflow based on deep learning was finally established, offering new ideas and techniques for marine organism health assessment and marine monitoring.

Waves are one of the most important phenomena in the ocean. The accurate and quick updated wave forecasting is of crucial significance for ensuring marine activities safety. The development of wave forecast is presented, including the traditional statistical wave forecasting methods, numerical wave prediction models, and the rapidly developing artificial intelligence (AI) wave forecasting methods. Currently, AI wave forecast models have been demonstrated unique advantages in terms of computational efficiency and adaptive forecasting accuracy, and they are gradually being applied in practical wave forecasting operations, transitioning from the research stage. However, they also have limitations, including limited forecasting elements, underestimation of extreme wave conditions, and weak forecasting generalization ability. Based on the characteristics of AI wave prediction, key scientific and technological issues that need to be addressed in current AI wave forecasting are proposed. These include efficient utilization of observational data, incorporation of prior physical knowledge, and enhancement of AI model safety and generalization ability.

Mangroves, salt marshes, and seagrass beds are known as the three major coastal blue carbon ecosystems (CBCEs), which play important roles in marine biodiversity maintenance, water purification, nutrient recycling, carbon sequestration and storage. Guangdong Province and Guangxi Zhuang Autonomous Region in China, where the CBCEs are widely distributed, were selected as the research areas, to investigate the spatial relationship between CBCEs and the marine ecological redlines (MERs). The CBCE conservation gap was analyzed, and the recommended priority conservation areas outside the MERs were proposed. The CBCE distribution data obtained from the satellite images in 2019, combined with field survey and UAV remote sensing data collected during 2020 and 2021 showed that the CBCEs in Guangdong totaled 14 481.39 hm² (mangroves 11 928.87 hm², salt marshes 1 258.00 hm², seagrass beds 1 294.52 hm²), whereas the CBCEs in Guangxi totaled 11 751.30 hm² (mangroves 10 171.70 hm², salt marshes 1 450.36 hm², seagrass beds 129.24 hm²). 62.13% of the CBCEs in Guangdong and 59.88% in Guangxi were covered by the MERs. The distribution areas and protection ratios of mangroves and seagrass beds in Guangdong were both larger than those in Guangxi, while the distribution area and protection ratio of salt marshes in Guangxi were larger than that in Guangdong. As to the 3 types of CBCEs in Guangdong, 62.13% of the mangroves, 38.16% of the salt marshes and 85.41% of the seagrass beds were under protection. For the CBCEs in Guangxi, 61.44% of the mangroves, 49.58% of the salt marshes and 52.99% of the seagrass beds were protected. This research suggests the coast from Rongmujiang Bay to Maowei Sea, the areas of Tieshan Bay, Leizhou Bay, Zhelin Bay and other related locations as the recommended priority conservation areas outside the MERs.

Coastline is one of the important geographical elements to describe the boundary between land and sea. Under the dual influence of natural factors and socio-economic factors, coastline dynamic evolution of different intensities continues to occur. Based on Landsat series satellite remote sensing images, the spatial and temporal evolution of the coastline of Nan’ao Island from 1976 to 2021 was analyzed by RS and GIS technology combined with field investigation, and the driving factors were analyzed by grey correlation analysis. The results show that : (1)In the past 45 years, the coastline of Nan’ao Island has changed significantly. The coastline length has increased by 11.06 km, and the fractal dimension have generally increased.(2)During the study period, the type of coastline changed from natural coastline dominated by bedrock to artificial coastline, the comprehensive index of coastline utilization show an increasing trend, and the main structure of coastline development and utilization show a form of single to multiple.(3)The evolution of the coastline of Nan’ao Island has obvious regional differences. That of Houzhai Town is greatly affected by human factors, and its evolution is more significant. The coastlines of Yun’ao and Shen’ao Towns are mainly affected by natural factors, and their evolutions are relatively slow.(4)Typhoon(natural disasters) and population are the main driving factors of the coastline evolution of Nan’ao Island.

An accuracy analysis of wind speed data retrieved from Sentinel-1 synthetic aperture radar (SAR) was conducted based on buoy observations from the National Data Buoy Center (NDBC). A back propagation (BP) neural network was utilized to correct the deviation in the SAR-derived wind speeds. Sensitivity experiments were designed for environmental factors, the number of training samples for BP neural network input, and neural network structure parameters. Finally, the SAR wind field data were converted into u and v vector wind data, and the accuracy analysis and correction were performed separately for u and v wind components. The experiment finds that the SAR-derived wind speed is underestimated compared to the buoy data. After calibration using BP neural network, the accuracy of SAR-derived wind speed data is improved, and the absolute value of bias of wind speed decreases from 0.78 m/s to 0.04 m/s, the RMSE of wind speed decreases from 1.98 m/s to 1.77 m/s. The sensitivity experiments suggest that low quality environmental factors input data will decrease the calibration effect of BP neural network, and increasing the sample size of the training set can improve that. The calibration results of converted u and v vector wind field data also show that the BP neural network has good correction effect.