The temporal and spatial variation characteristics and influencing factors of euphotic depth and primary productivity in the Zhanjiang Bay were studied by applying voyage data of four seasons from 2016 to 2017．The results showed that the average value of euphotic depth was (6.95±3.17) m, and the spatial variations were more obvious than the seasonal variations. There was a significant positive correlation between K_d (PAR) and turbidity, as the R² of established linear regression model was 0.73 (p<0.01), indicating that suspended particles on euphotic depth was the dominant attenuation factor in the Zhanjiang Bay. The average value of primary productivity obtained by VGPM model was (639.53±427.95) mg/(m²·d), and its temporal and spatial characteristics were basically consistent with euphotic depth, which mean euphotic depth explained the temporal and spatial distribution pattern of primary productivity better than chlorophyll a concentration.

In order to improve the study of the interaction between internal solitary waves and submarine slope sediments, the stage of continued motion on the slope after internal solitary waves fragmentation is focused on in this paper, and conducts physical simulation experiments to analyse the changes in earth pressure and super-pore water pressure in response to the slope to reveal the process of internal wave action. The results show that the sediment particles on the slope are resuspended under the combined action of vortex and seepage caused by the internal solitary waves fragmentation, and the change in slope gradient does not change the dominant dynamic role of the sediment in generating the dynamic response; the amplitude of the internal solitary waves affect the ratio between vortex and seepage, i.e. the vortex is dominant under small amplitude conditions and the seepage is dominant under large amplitude conditions; the fragmented fluid forms a new dynamic role when it rushes out along the slope. The dynamic response of the sediment to the new vorticity is influenced by the slope of the slope. The results of this paper are useful for the study of internal solitary waves resuspension transporting seafloor sediments and modifying seafloor topography.

In order to understand the distribution characteristics, relationships and diffusion fluxes of nutrients and metal elements across the sediment-water interface, we investigated the vertical profiles of nutrients and metal elements through the diffusive gradient in thin-films (DGT) technology in Shenzhen Bay (SZB) and Maozhou River (MZR). The two-dimensional high-resolution available sulfur (DGT-labile S) distributions were also synchronously measured. The results showed that the mean concentrations of ${\rm{NH}}_4^+ $, P and S²⁻ and heavy metals of Cd, Co, Cu, Fe, Ni, Pb and Zn in porewater of MZR were significantly higher than those in the overlying water and the SZB, except for ${\rm{NO}}_3^- $ and Mo. The pollution degree of nutrients and metal elements in porewater of MZR was generally higher than that in SZB, but the pollutants of nutrients and metal elements in SZB were more than those in MZR. The spatial distribution of DGT-labile S in MZR indicated the distinguish bioturbation-induced tubes, which enhanced the spatial heterogeneity and led the increasing of solute concentrations with sediment depths. The diffusion fluxes of nutrients and metal elements ranged from −0.27 μg/(cm²·d) to 0.0065 μg/(cm²·d) in SZB, and from −0.061μg/(cm²·d) to 0.069 μg/(cm²·d) in MZR.

In this study, four samples of Gymnothorax mucifer were collected from the fishery market of Xiamen City, Fujian Province during the year 2020 to 2021, which were newly recorded in the coastal waters of China. Previously, the species had only been recorded in Australia and Hawaii, and was considered to be a synonym of Gymnothorax kidako. Detailed morphological characteristics of four G. mucifer species were analyzed, and the molecular identifications as well as phylogenetic constructions were also carried out basing on DNA barcode COI gene in this study. The main distinguishing characteristics of G. mucifer were as follow: the colour of the body was yellowish brown, the front of the head was slightly purple, the whole body was covered with slender, sparse, irregular branch-liked brown marking and the markings became darker and thicker near the tail, forming clear net-liked patterns; the margin on the anal fin was white, and became serial pale blotches on posterior part of the tail; total length was 1.01 times of standard length and 8.00−8.39 times of head length; the maxillary teeth were 8−10 and dentary teeth were 14−20 on each side, both teeth were uniserial; the median inter maxillary teeth were slender and uniserial; the total vertebrae were 117−139 and mean vertebral formula was 6-47-130. Basing on the COI gene analysis, the genetic distance between G. mucifer and G. kidako was 0.074, which was greater than the value 2% (0.020) suggested by Herbert as minimum genetic distance value to distinguishing different species, revealing that the two species might be two independent species. Morphologically, G. mucifer could also be distinguished from G. kidako by certain external characteristic: the body markings of G. mucifer were slender, sparse and inconspicuous, the front of the head was slightly purple, and the white margin of the anal fin broke into series of pale blotches on posterior part of the tail; the markings of G. kidako were obvious and thick with darker colour, the front of the head was yellow-white, the white margin on the anal fin continuous to the tip of the tail. The results of the study provided a taxonomic basis for the systematic classification and the species list revision of the Gymnothorax fish in our country.

Current CMIP6 climate models (such as CESM2 and NESM3) use constant snow density, while those models that focus on snow depth and density changes (such as SnowModel-LG) use empirical snow density formulas. Comparing the modeled snow depth with those observed by the CryoSat-2 satellite, it is found that from the perspective of the spatial distribution and average value of the snow depth, it is difficult to detect the effects of varying snow density on the simulation of snow depth in the Arctic Ocean. The model improvement and its mechanism from varying snow depth is still to be further studied. Here an empirical snow density model considering meteorological factors such as air temperature, wind etc., is applied to the SNOTEL observational site to carry out the following sensitivity experiments for different factors: A. snow density model considering all meteorological factors; B. constant snow density model; C. same as A but the influence of wind on the densification is not considered and D. same as A but the influence of temperature on the densification is not considered. The root mean square error of snow depth simulated by experiments A, B, C and D from November 1, 2018 to May 10, 2019 are 4.2 cm, 4.8 cm, 25.9 cm, and 4.2 cm, respectively. The results show that the mean snow density and depth simulated by the varying snow density model are close to the results using constant snow density, but the root mean square error of the simulated snow depth from Case A is the smallest, and the Case A simulation can reproduce the high frequency variations of snow depth on the time scale of several days to ten days. In the meantime, the relative errors in the period with high-frequency snow depth variations are also reduced as they are found to be related. In addition, it is also found that the influence of temperature on snow densification is much smaller than that of wind.

Sillago sihama is an important fishery species in China and plays an important role in the marine ecosystem of the Yellow Sea. Species distribution models can be used to predict its distribution by establishing the relationships between its abundance and environmental factors. However, due to high mobility of the marine animals, the relationship between their distribution and environmental factors is often nonlinear and variable with spatial locations. Based on data collected from bottom trawl survey in the Shandong coastal waters in autumn of 2016, both generalized additive model (GAM) and geographically weighted regression (GWR) model were used to analyze nonlinear and spatial nonstationary relationships between distribution of the species and environmental factors, and results from the two models were compared. Results from the GAM indicated that the main environmental factors were depth, sea bottom temperature and salinity, and depth had the largest deviance explained (23.50%). GWR model results showed that there were spatial non-stationary relationships between distribution of the species and depth and sea bottom temperature. GWR model results indicated a negative correlation between depth and biomass of the species, and a positive correlation between sea bottom temperature and biomass of species. Regarding performance of the models, GWR model showed advantages over GAM in identifying influencing factors and predicting distribution, and GWR model was recommended for use in similar applications.

With the intensification of global warming, the source sink process of carbon in the Arctic shelf-edge sea is becoming more and more important in the study of global carbon cycle. As a typical continental shelf marginal sea in the Arctic Ocean, the source, transportation and burial of sedimentary organic carbon in this area are unique under the influence of rivers, sea ice, marine primary productivity and coastal erosion. Based on the sampling of suspended particulate matter (SPM) and hydrological data obtained from the second Sino-Russian Arctic joint expedition during late summer and early fall in 2018, we foucus on the distribution characteristics, sources and influencing factors of particulate organic carbon (POC) in the Laptev Sea. The results show that POC ranges from 35.27 μg/L to 1 185.58 μg/L, with an average of 172.65 μg/L. Under the effect of river input, coastal erosion and marine primary productivity, the distribution of surface POC shows a decreased trend from near shore towards offshore; the bottom POC is mainly controlled by sediments resuspension, and the high content of POC appears in the east of Lena River Delta. There was a significant positive correlation between SPM concentration and POC concentration, indicating its direct impact on the occurrence of POC; a more positive relation is found among the bottom layer samples, which may indicate the varied origin of POC in different layers. The value of δ¹³C_POC in study area value is between −31.03‰ and −25.79‰, and the value of δ¹³C in surface layers is obviously depleted compared with the bottom layer, which is even lower than the end-member of the surrounding terrestrial contributor, suggesting that these POC is not derived from land-based origin. The utilization of the terrestrial POC degraded dissolved inorganic carbon by offshore phytoplankton maybe responsible for this depletion of δ¹³C offshore, which could also be an important process on the supply and source apportionment of POC in this Arctic coastal area.

The loading tides in the South China Sea and adjacent straits are calculated by means of the Green’s function method based on a high-resolution regional ocean tide model for the South China Sea, the DTU10 global ocean tide model, and the Gutenberg-Bullen A Earth model. The results show that the maximum amplitude of M₂ vertical displacement loading (VDL) tide exceeding 18 mm appears in the Taiwan Strait; the second maximum exceeding 14 mm appears off the north-west coast of Kalimantan. The maximum amplitudes of the K₁ and O₁ VDL tides, exceeding 18 mm and 14 mm respectively, appear near the southern South China Sea; the second maximum exceeding 8 mm, appears in the Beibu Gulf. The distribution patterns of self-attraction and loading (SAL) tides are very similar to those of VDL tides in that the SAL tides have amplitudes about 1.2 times to 1.7 times the corresponding VDL tides and have phases basically opposite to corresponding VDL tides. The maximum amplitude of M₂ SAL tide also appears in the Taiwan Strait and off the north-west coast of Kalimantan, with a magnitude exceeding 24 mm and 18 mm respectively.

The internal solitary wave and mesoscale eddy are common mesoscale dynamic processes in the northern South China Sea. In this paper, we use the Terra/Aqua-MODIS, ENVISAT ASAR and multi-source satellite altimeter data from 2010 to 2015 to realize remote sensing of isolated waves and mesoscale eddy in the South China Sea, and analyze the influence of mesoscale eddy on the propagation direction of internal solitary wave. The results show that the mesoscale eddy and the internal solitary wave coexist mainly in the northeastern part of the South China Sea. When the two coexisted, the cyclone (cold eddy) caused the internal solitary wave to deviate from the original propagation direction and propagation and spread to the west-south direction. The anticyclonic (warm eddy) causes the internal solitary wave to spread westward to the north, and the cyclone and the anticyclone change the direction of the internal solitary wave propagation is just opposite. The coexistence time of internal solitary wave and mesoscale eddy is mainly concentrated from March to September, and the propagation direction of internal solitary wave is almost unchanged in March due to the interaction of cyclone and anticyclone. In April and May, the internal solitary wave deviated from its original propagation direction and propagated southward mainly due to cyclone. From June to September, the internal solitary wave deviated from its original direction and propagated northward, mainly under the influence of anticyclone. In April and May, the internal solitary wave deviated from its original propagation direction and propagated southward mainly due to cyclone. From June to September, the internal solitary wave deviated from its original direction and propagated northward, mainly under the influence of anticyclone. The effect of mesoscale eddy on the propagation direction of internal solitary wave is investigated by remote sensing, and the results are in agreement with the field observation.

Tsunami is one of the disasters that seriously endanger the safety of human life and property among natural disasters. In the context of global warming and increasing economic development, more and more people, infrastructure and wealth are exposed to tsunami disasters, greatly increasing the risk and vulnerability of personal and property safety in coastal and delta areas. The analysis of temporal and spatial variation of historical tsunami disasters can help us understand the evolutionary laws of tsunami disasters, and provides a useful reference for more accurate disaster warning, disaster prevention and control, etc. A study on the temporal and spatial variation of global tsunami by extracting complete and homogeneous data is conducted in this paper. The results show that: (1) for 0.1 m≤RH<0.5 m, 0.5 m≤RH<1 m, 1 m≤RH<5 m, 5 m≤RH<10 m, 10 m≤RH<20 m and 20 m≤RH intervals, the tsunami catalogues can be considered complete since 1963, 1940, 1950, 1946, 1922 and 1885 respectively; (2) from time changes it can be seen that there is a certain increasing trend in the occurrence of global tsunamis. Approximately 7 more wave runup events are observed every year. At the same time, in different intensity intervals, the frequency of tsunamis has different changes. In the intervals of 0.1 m≤RH<0.5 m, 0.5 m≤RH<1 m, and 1 m≤RH<5 m, the tsunami have a certain periodicity, showing two obvious peaks, but when the RH is greater than 5 m, the periodicity of the tsunami is no longer obvious, and it shows a clear increasing trend at this time; (3) there is a certain increasing trend in the occurrence of tsunamis in East Asia, South Pacific, South America, and Indian Ocean. However, in North America, there is a decreasing trend, and there is no significant change in Europe; (4) except for North America, tsunami events in other regions show a good power law distribution relationship, indicating that the occurrence of tsunamis follows certain self-organized critical behavior. In comparison, small tsunami events are more likely to occur in Europe, while tsunami events in East Asia and the Indian Ocean are more prone to various types of tsunami events, of which large tsunami events occupy a larger portion.