Optically stimulated luminescence (OSL) dating as a absolute dating method for sediments has been widely used in Quaternary studies. Improving the accuracy and precision of OSL dating has been a hot spot in academic research. However, for a long time, there have been numerous studies on equivalent dose measurement procedures and calculation methods, and relatively few studies on environmental dose rates. In this paper, we conducted OSL chronology study on marine sediment samples obtained from the Borehole ZBW in the southwestern of Hainan Province. The contents of uranium, thorium and potassium in the samples were measured by inductively coupled plasma mass spectrometry/optical emission spectrometry (ICP-MS/OES), neutron activation analysis (NAA) and γ-ray spectrometer, respectively. The results show that there are differences in the results measured by the three methods in some strata, and the reasons could be: (1) incomplete dissolution of U in heavy minerals during the ICP-MS/OES pretreatment process; (2) the ICP-MS/OES measurement results are the elemental contents of U, Th, and K, which include some non-radionuclide contents; (3) the small sample size used for NAA, which was subject to chance errors, and the dilution effect of non-radioactive material in the sample affects the measurement results; (4) there is an imbalance of uranium system in the sample. Combined with the sedimentary environment in the study area and the data measured by HPGe γ-ray spectrometer, the results show that the uranium imbalance originates from the autogenous uranium absorption phenomenon, and the effect on the environmental dose rate is about 11%.

Shapes of freak waves are strongly related to the characteristics of their surrounding sea state. The most probable shape of rogue waves obeys NewWave theory under the narrow band assumption in the linear process. Based on the NewWave theory, the largest wave is located in the center of a wave group and the adjacent waves are symmetrical. However, wide spectral widths containing various frequency components are more common in the ocean. There is still a lack of systematic understanding of the shapes of in-situ measured freak waves and the surrounding waves. Furthermore, key parameters that affect the shapes are not clear. In the present study, shapes of 112 freak waves and the related influence factors from the ocean weather station in the Norwegian Sea are investigated. Merely 52% of freak waves are located in the center of the group, and the possibility of other freak waves occurring towards the front of the wave groups is higher. Besides, shapes of the adjacent prior and following waves of freak waves are asymmetry. Generally, the amplitude of the following wave is larger than that of the preceding one. By quantitatively comparing shapes of the averaged measured rogue waves and based on the NewWave theory, it is found that the spectral width is a key parameter for affecting shapes of freak waves. With the spectral width wider, the difference of profiles of sea surface elevations around the freak waves between the measured and using the NewWave theory is exponentially increasing.

The free-dropping acoustic Doppler current profile (FADCP) samples in a “free-fall” manner, which can observe the full depth current without relying on the cable traction of the ship, and its stability is greatly improved compared with lowered acoustic doppler current profiler (LADCP), effectively reducing the irregular motion in the observations. The FADCP observation experiments conducted in April and September 2021 at Xisha waters in the South China Sea obtained current and CTD data from two sections containing 16 stations. Based on the real bottom current observation during the resting period, the full-depth current profiles at each station were obtained by shear method, and the average deviation of the station profile near a mooring compared with the mooring profile was 3 cm/s. The observation section captured the cyclonic eddies of two periods at Xisha waters with more precise vertical structures than the HYCOM simulation, and the surface flows fitted with the absolute geostrophic currents. This research shows that the FADCP has low vessel requirements, high data quality, easy post-processing and good results, but cannot perform supplementary observations for specific water layers.

Coral reef substrate classification plays a crucial role in marine resource development and marine ecological protection. At present, deep learning semantic segmentation methods are widely used in the field of remote sensing image classification, but less research has been conducted in substrate classification. Due to the high cost of pixel-by-pixel labeling in the fully supervised deep learning-based method, it is not suitable for large-scale and high-frequency substrate classification work. The semi-supervised deep learning-based method can effectively use the labeled labels to generate pseudo-labels for unlabeled data, thus effectively reducing the labor cost, however, the performance of the existing semi-supervised method is vulnerable to the interference of pseudo-label noise. To address the above problems, this paper proposes a semi-supervised substrate classification method based on soft and hard collaborative decision making. First, a high quality Pseudo tag is generated using joint decision making of multiple models; then, a loss function (Collaboration Choice of decision Confidence Loss function, 3CLoss) is proposed to take into account the confidence of Pseudo tag pixels and guide the model for training; finally, a soft and hard collaborative decision making approach is used to obtain accurate substrate classification results. The accuracy of this paper was evaluated on the shallow benthic habitat atlas datasets of Buck Island Reef in the northern part of St. Croix, U.S. Virgin Islands, and Pearl and Hermes Atolls, about 400 km southeast of Midway Island, Hawaiian Islands, and the experimental results show that the accuracy of the proposed method is comparable to that of the fully supervised learning method, and 3.08% higher than that of the mainstream semantic segmentation methods on average, which can effectively serve the coral reef substrate survey.

Water temperature prediction is a key technology to ensure the production of coastal fisheries and environmental safety. The existing numerical models have high development costs with insufficient business applications. This study develops a prediction method of water temperature through integrating differential regression (DR) and transferable long short-term memory (TLSTM). Taking the water temperature of Xiamen Bay (source domain, with a large number of data) and Sansha Bay (target domain, with less data) as the research object, the DR model is established based on the data of monitoring water temperature and forecast temperature in the Sansha Bay, and the TLSTM model is established based on the long-term monitoring data of water temperature in the Xiamen Bay. The pure differential regression model, mixed differential regression model and TLSTM model are integrated into the DR-TLSTM model of Sansha Bay by using variable weight algorithm, and the performance of the model is evaluated, the results are compared with the LSTM model based on only a small amount of monitoring data in the Sansha Bay. The results show that: (1) the prediction accuracy of TLSTM model is better than that of LSTM model based on a small amount of data in the target domain; (2) the DR-TLSTM model has high prediction accuracy, and the root mean square error of prediction in the next 1−7 days is 0.13−0.77℃, and the root mean square error of prediction in the next 1−3 days is less than 0.4℃; (3) the DR-TLSTM model can effectively predict the sudden rise or fall trend of water temperature, and the root mean square error of predicting the sudden change point of water temperature is 0.29−1.09℃. Based on the DR-TLSTM model, the operational information service of water temperature early warning and forecast in the Sansha Bay is realized.

Using the tropical cyclone (TC) best track data from the Shanghai Typhoon Research Institute of the China Meteorological Administration (CMA-STI) and the monthly mean reanalysis data of NCEP/NCAR, the interannual variability of the basin-scale large-scale environmental steering flow and the tropical cyclone activity in the western North Pacific (WNP) during peak season from July to September from 1979−2016 are investigated. The results show that: (1) There are two typical modes of summer large-scale environmental steering flow in the WNP at the inter-annual scale. The first typical mode is a dipole circulation with a meridional distribution, which is closely related to the eastern ENSO and the sea-air coupling mode in the WNP region. (2) The TC activity (generation location, tracks, intensity and duration) differs significantly between the two typical interannual mode anomaly years of the large-scale environment steering flow, but the differences have distinctly different characteristics for the two typical inter-annual modes. (3) The spatial distribution of TC generation location shows significant differences from north to south between the years of the first typical interannual mode anomalies of large-scale environment steering flow; the TC tracks, especially the northwestward and westward prevailing tracks, also have significant differences, and their average duration and intensity also show their corresponding significant differences. In the second major interannual mode anomaly years, the TC generation locations show significant east-west distribution especially in the southeast quadrant, and the differences in TC tracks are mainly in the northwestward and offshore steering prevailing tracks, and their mean durations and intensities also show significant differences.

Diurnal observation is necessary for grasping the variability of carbonate system in coastal waters and sea-air CO₂ exchange process and is helpful to reduce the uncertainty of assessments for carbon source/sink. Surface carbonate system and related parameters were obtained during twice 24 hours fixed sampling and observation conducted in April and August 2018 in the Yingluo Bay-Anpu Harbor, located in the northeastern Beibu Gulf. In this paper, we analyzed the hourly variations of partial pressure of CO₂ in surface sea water (pCO₂) and discussed the corresponding environment factors controlling pCO₂ in both seasons. The pCO₂ values ranged from 530−628 μatm in spring to 427−748 μatm in summer, with the average sea-air CO₂ flux in spring and summer for (1.7±0.8) mmol/(m²·d) and (1.2±0.8) mmol/(m²·d), respectively. The study area acted as a weak CO₂ source during both seasons. The hourly changes of pCO₂ in spring were more significantly affected by temperature effects than in summer. During summertime, pCO₂ had more sensibly response to tidal action, enhanced biological production and respiration with inflow of coastal freshwater such as rivers and submarine groundwater discharge. Water warming dominated the formation of high pCO₂ in spring. The enhanced biological production during the physical mixing of saline and fresh water played a role in the drawdown of surface dissolved inorganic carbon (DIC) and the mangroves and salt marshes ecosystems along the bay had a certain contribution to the addition of DIC on the freshwater end-member in summer. The variations of the ratio of DIC concentration and total alkalinity (TA) in the water masses could imply the overall distribution pattern adjacent to the Yingluo Bay-Anpu Harbor that high values exists in the bay and lower values exists in offshore water.

The majority of global fish stocks lack adequate data for their stock statuses to be assessed using conventional stock assessment methods. Data-limited methods, such as CMSY, have been increasingly recommended as new solutions for stock assessment and fishery management. However, CMSY is highly dependent on data quality, and the reliability of the method is yet to be verified under circumstances of limited length of time series data and variable observational errors. In this study, we investigated effects of lengths of catch time series, stages of fishery development, and levels of observational errors in catches on stock assessment of three economically-important species in the Yellow Sea using CMSY method. The results show that chub mackerel (Scomber japonicus), hairtail (Trichiurus lepturus), and silver pomfret (Pampus argenteus), all have been overfished (B/B_MSY<1 and F/F_MSY>1), with their yields higher than estimated MSY since 2000, and although their fishing intensities have been reduced over the most recent decade, their biomasses remain at low levels (B/B_MSY<1). The retrospective analysis show small differences in the results of stock assessment for the three species, indicating that the assessments are robust enough with long time series data. As to effects of lengths of catch time series, the assessments are more stable using time series data covering a period of both rise and fall in catches. The effect of observational errors in catches is also tested, showing that when the error is >20%, the model tend to overestimate MSY and B_MSY, but the assessment remains robust enough. This study suggests that cautions should be undertaken in the application of CMSY by using longer time series of catch data and, in the presence of high uncertainty in the assessment, more conservative measures should be taken in fishery management.

Yellowfin tuna (Thunnus albacares) is one of the most important fishes with great global economic and ecological value, and its conservation and management have received much concerns. The stock status of yellowfin tuna in the Indian Ocean based on the age-structured assessment program model is evaluated in this study, focusing on the uncertainties of its life history characteristics on the stock assessment results. The results show that the resources of yellowfin tuna in the Indian Ocean remained relatively stable from 1960 to 1985 and then declined gradually, while the fishing mortality coefficient F increased rapidly after 2010. This stock in 2020 may be overfished, since the estimated F₂₀₂₀ was greater than F_MSY (F that could attain maximum sustainable yield MSY), while spawning stock biomass, SSB₂₀₂₀ was less than SSB_MSY. Sensitivity analysis was also conducted to evaluate the uncertainties of stock assessment. Two important life history characteristics, natural mortality M and steepness of spawning-stock relationship h, were analyzed for their influence on the estimates of F, SSB and biological reference points. When h was set to 0.7, 0.8, and 0.9, SSB_MSY and SSB₀ (the unfished SSB) reduced by about 255 300 t and 340 400 t; and F₂₀₂₀/F_MSY gradually decreased (from 2.88 to 2.21 and 1.73). When the M was set to M₁ (0.963, 0.663, 0.548, 0.493, 0.463, 0.446) and M₂ (1.068, 0.735, 0.608, 0.547, 0.514, 0.495) respectively, the larger M₂ leads to lower SSB and F₂₀₂₀/F_MSY. In summary, the conservation and management of Indian Ocean yellowfin tuna should be tightened in the future to achieve long-term sustainable development of this fishery. The life history characteristics of yellowfin tuna should be fully understood, especially M and h estimation should be improved, to provide more accurate information for stock assessment and fisheries management for Indian Ocean yellowfin tuna.

In order to improve the success rate and accuracy of species identification of fish eggs and larvae, fish eggs and larvae samples collected from the Zhujiang River Estuary in spring were identified by DNA barcoding technology based on the mitochondrial COⅠand 12S rRNA genes. A total of 391 samples were amplified and 60 species in 7 orders, 25 families, 42 genera were successfully identified (2 species were not identified). Among them, Perciformes had the most species and quantity accounting for 51.6% and 47.91% respectively, followed by Clupeiformes with 25% and 34.56% respectively. There were 10 dominant species, among which Coilia mystus had the highest dominance of 0.071, Collichthys lucidus had the lowest dominance of 0.014. The amplification results of COⅠand 12S rRNA gene fragments showed that the success rate of 12S rRNA gene amplification in eggs and larvae (95.60%) was significantly higher than that of COⅠgene (43.22%). Genetic distance and ABGD analysis showed that the intraspecies genetic distance of COⅠ gene was 0−0.005 (average was 0.003), and the interspecific genetic distance was 0.061−0.376 (average was 0.253), and there was an obvious “barcode gap” between them. The intraspecific genetic distance of 12S rRNA gene was 0−0.011 (average was 0.007), and the interspecific genetic distance was 0.007−0.487(average was 0.283). which do not form a “barcode gap” between the interspecific and intraspecies genetic distances. The Bayesian phylogenetic tree based on COⅠ and 12S rRNA genes showed that all species could be clustered into independent branches and can be effectively distinguished. The above results show that both COⅠ gene and 12S rRNA gene can be used for the identification of most fish eggs and larvae, but the success rate of COⅠ gene amplification is low, and it is difficult to distinguish some closely related species of 12S rRNA gene. The combined use of the two genes can improve the success rate and accuracy of species identification of fish eggs and larvae.