Yellow Sea Research Articles

Baseline radon concentration in seawater-seabed interface of the central Yellow Sea: implications for monitoring offshore geological CO2 storage site

Baseline radon concentration in seawater-seabed interface of the central Yellow Sea: implications for monitoring offshore geological CO₂ storage site

Thalassospira aquimaris sp. nov. and Winogradskyella marincola sp. nov. two marine bacteria isolated from an agar-degrading co-culture.

Two novel Gram-stain-negative, aerobic, and non-motile strains, designated FZY0004T and YYF002T, were isolated from an agar-degrading co-culture, which was obtained from seawater of the intertidal zone of Yancheng City, the Yellow Sea of China. Strain FZY0004T optimally grew at 28°C, pH 7.0, and 2-6% NaCl, while strain YYF002T optimally grew at 28°C, pH 7.5, and 2-4% NaCl. Strain FZY0004T possessed Q-9 as the major respiratory quinone, and its major fatty acids (> 10%) were summed feature 8 (C18:1 ω7c), C16:0, and summed feature 3 (C16:1 ω7c/C16:1 ω6c). The polar lipids identified in strain FZY0004T were phosphatidylethanolamine (PE), phosphatidylglycerol (PG), and several unidentified phospholipids (PL) and lipids (L). On the other hand, strain YYF002T had MK-6 as the predominant respiratory quinone and its major fatty acids consisted of iso-C15:0, iso-C15:1 G, and iso-C15:0 3-OH. The polar lipids identified in strain YYF002T were aminolipid (AL), PE, and several unidentified lipids. Strain FZY0004T shared 99.5% 16S rRNA gene sequence similarity and 90.1% average nucleotide identity (ANI) with T. povalilytica Zumi 95T, and strain YYF002T shared 99.2% 16S rRNA gene sequence similarity and 88.2% ANI with W. poriferorum JCM 12885T. The genomic DNA G + C contents of strains FZY0004T and YYF002T were 54.5% and 33.5%, respectively. The phylogenetic, phenotypic, and physiological characteristics permitted the distinction of the two strains from their neighbors, and we thus propose the names Thalassospira aquimaris sp. nov. (type strain FZY0004T = JCM 35895T = MCCC 1K08380T) and Winogradskyella marincola sp. nov. (type strain YYF002T = JCM 35950T = MCCC 1K08382T).

Spatial-temporal impacts of invasive Spartina anglica on the rates and pathways of organic carbon mineralization and resulting C-Fe-S cycles in the intertidal wetland of the Han River Estuary, Yellow Sea

To elucidate the spatial-temporal impact of invasive saltmarsh plant Spartina anglica on the biogeochemical processes in coastal wetlands, we investigated the rates and partitioning of organic carbon (Corg) mineralization in three representative benthic habitats: (1) vegetated sediments inhabited by invasive S. anglica (SA); vegetated sediments by indigenous Suaeda japonica; and (3) unvegetated mud flats. Microbial metabolic rates were greatly stimulated at the SA site during the active growing seasons of Spartina, indicating that a substantial amount of organic substrates was supplied from the high below-ground biomass of Spartina. At the SA site, sulfate reduction dominated the Corg mineralization pathways during the plant growing season, whereas iron reduction dominated during the non-growing season. Overall, due to its greater biomass and longer growing season than native Suaeda, the expansion of invasive Spartina is likely to greatly alter the Corg-Fe-S cycles and carbon storage capacity in the coastal wetlands.

How do peak-water-levels respond to natural shoreline restoration projects with progressive tide parallel to shore

Facing strict control of natural shoreline retention and the urgent need for mitigation of oceanic hazards, amid climate change, challenges persist in the spatial allocation of nature-based and concrete-based measures in China's coastal protection and restoration plans. This study explores the feasibility of “Operational Landscape Units" (OLUs) and their impact on peak-water-levels (PWL) and tides on the western Yellow Sea inner shelf (WYS), considering the newly published “China Coastal Ecological Zoning on Land and Seas” in 2023. Numerical experiments with a single OLU of county-size operated along the WYS indicate the largest reduction of 10% (0.23m) in PWL on the coast happens when an OLU is set on the section closest to the tidal amphidrome and the shoreline is parallel to the tidal propagation direction. Furthermore, as the restoration section extends on both sides, the spatial scale of the changed PWL expands. Further investigation reveals that OLUs positioned upstream of the tidal propagational direction can more effectively mitigate coastal inundation risks. This is because local tidal damping through bottom friction inside the OLUs overweighs the tidal damping on the inner shelf outside the OLUs. Overall, this study provides insights into high-tidal-flooding mitigation through OLUs in China's coastal zones, offering references for future coastal plans and flood control deployments.

Umbrella effect of spotted seal conservation and achieving 30 × 30 targets in the Yellow Sea ecoregion

Umbrella effect of spotted seal conservation and achieving 30 × 30 targets in the Yellow Sea ecoregion

Accumulation of trace metal elements in ophiuroids with different feeding types in the North Yellow Sea

Ophiuroids, as an important group of echinoderms, are widely distributed in marine benthic habitats. Previous studies have identified two primary feeding types of ophiuroids in the Yellow Sea, including carnivorous (Ophiura sarsii vadicola and Stegophiura sladeni) and suspension feeders (Ophiopholis mirabilis). Despite their ecological role in the benthic food webs, little is known about their accumulation of trace metal elements (TMEs). In this study, the content of TMEs (Pb, As, Cd, Hg, Cr, Cu, Zn), methylmercury (MeHg) and δ15N value of three ophiuroids species from the North Yellow Sea were determined. Our results showed that the contents of some TMEs (As, Cd, Cr, Cu and Zn) and MeHg were significantly different in three species of ophiuroid (p < 0.05). There were significant correlations between the accumulations of trace metal elements (Pb, Cd and Zn) and the δ15N value of the ophiuroids (p < 0.05). Additionally, As and Zn exhibited opposite correlations in ophiuroid with two feeding types, which may be related to their host species and different feeding habits. This study provided fundamental data for understanding the distribution of trace metal elements in echinoderms.

Distribution, abundance, and realized niches of meroplankton by two different mesh size nets during spring 2017 in the Southern Yellow Sea, China

Distribution, abundance, and realized niches of meroplankton by two different mesh size nets during spring 2017 in the Southern Yellow Sea, China

Spatial distribution of lipophilic shellfish toxins in seawater and sediment in the Bohai Sea and the Yellow Sea, China

Lipophilic shellfish toxins (LSTs) are widely distributed in marine environments worldwide, potentially threatening marine ecosystem health and aquaculture safety. In this study, two large-scale cruises were conducted in the Bohai Sea and the Yellow Sea, China, in spring and summer 2023 to clarify the composition, concentration, and spatial distribution of LSTs in the water columns and sediments. Results showed that okadaic acid (OA), dinophysistoxin-1 (DTX1) and/or pectenotoxin-2 (PTX2) were detected in 249 seawater samples collected in spring and summer. The concentrations of ∑LSTs in seawater were ranging of ND (not detected) –13.86, 1.60–17.03, 2.73–17.39, and 1.26–30.21 pmol L−1 in the spring surface, intermediate, bottom water columns and summer surface water layers, respectively. The detection rates of LSTs in spring and summer seawater samples were 97% and 100%, respectively. The high concentrations of ∑LSTs were mainly distributed in the north Yellow Sea and the northeast Bohai Sea in spring, and in the northeast Yellow Sea, the waters around Laizhou Bay and Rongcheng Bay in summer. Similarly, only OA, DTX1 and PTX2 were detected in the surface sediments. Overall, the concentration of ∑LSTs in the surface sediments of the northern Yellow Sea was higher than that in other regions. In sediment cores, PTX2 was mainly detected in the upper sediment samples, whereas OA and DTX1 were detected in deeper sediments, and LSTs can persist in the sediments for a long time. Overall, OA, DTX1 and PTX2 were widely distributed in the water column and surface sediments in the Bohai Sea and the Yellow Sea, China. The results of this study contribute to the understanding of spatial distribution of LSTs in seawater and sediment environmental media and provide basic information for health risk assessment of phycotoxins.

On the genus Lyphira Galil, 2009 (Crustacea: Brachyura: Leucosiidae), with descriptions of four new species from the West Pacific

The taxonomy of the shallow water leucosiid crab, Lyphira heterograna (Ortmann, 1892), is revised on the basis of the lectotype and material from Japan, southern China, Hong Kong and Southeast Asia previously identified with this species. Six species are now recognised in the Lyphira heterograna species-group, of which four are new species: L. chomel n. sp., L. ngankee n. sp., L. linda n. sp., and L. bellagrana n. sp. Philyra acutidens Chen, 1987, described from the Yellow Sea and previously synonymized with L. heterograna, is recognised as a valid species. These species are redescribed, figured and compared with congeners.

Anthropocene 129I Record in the Yellow Sea Sediments and Its Indication for River-Delivered Radioactive Pollution to Marginal Seas.

As the number of coastal nuclear facilities rapidly increases and the wastewater from the Fukushima Nuclear Plant has been discharged into the Pacific Ocean, the nuclear environmental safety of China's marginal seas is gaining increased attention along with the heightened potential risk of nuclear accidents. However, insufficient work limits our understanding of the impact of human nuclear activities on the Yellow Sea (YS) and the assessment of their environmental process. This study first reports the 129I and 127I records of posthuman nuclear activities in the two YS sediments. Source identification of anthropogenic 129I reveals that, in addition to the gaseous 129I release and re-emission of oceanic 129I discharged from the European Nuclear Fuel Reprocessing Plants (NFRPs), the Chinese nuclear weapons testing fallout along with the global fallout is an additional 129I input for the continental shelf of the YS. The 129I/127I atomic ratios in the North YS (NYS) sediment are significantly higher than those in the other adjacent coastal areas, attributed to the significant riverine input of particulate 129I by the Yellow River. Furthermore, we found a remarkable 129I latitudinal disparity in the sediments than those in the seawaters in the various China seas, revealing that sediments in China's marginal seas already received a huge anthropogenic 129I from terrigenous sources via rivers and thus became a significant sink of anthropogenic 129I. This study broadens an insight into the potential impacts of terrigenous anthropogenic pollution on the Chinese coastal marine radioactive ecosystem.

Interannual variability of air-sea CO2 exchange in the Northern Yellow Sea and its underlying mechanisms

A three-dimensional (3-D) physical-biogeochemical-carbon cycle coupled model is used to investigate the interannual variability of the air-sea carbon dioxide (CO2) flux (FCO2) in the Northern Yellow Sea (NYS) from 2009 to 2018. The verification of the model indicate that the simulation results for multiple variables exhibit consistency and fit well with the observed data. The study show that although the multi-year average FCO2 in the NYS is close to the source-sink balance, there are obvious interannual differences between different years. In particular, a relatively strong source of atmospheric CO2 (1.0 mmol m–2 d–1) is exhibited in 2014, while a relatively strong sink of atmospheric CO2 (–0.7 mmol m–2 d–1) emerges in 2016. Mechanism analysis indicates that the abnormally high temperature is the main controlling factor for the relatively high CO2 efflux rate in the NYS in 2014, while the abnormally low dissolved inorganic carbon (DIC) concentration is the main factor contributing to the relatively high CO2 influx rate in 2016. Further analysis reveals that the primary reason for the low DIC concentration since the onset of winter in 2016 is the high net decrease rate of DIC in the NYS in 2015, influenced by net community production in the summer and advection processes during the autumn. The abnormally high primary production during the summer of 2015 results in the excessive reduction of DIC concentration through biological processes. In addition, due to the strong northeasterly wind event in November 2015, low-concentration-DIC water from the Yellow Sea (YS) extends into the Bohai Sea (BS). This further leads to higher DIC flux from the NYS into the BS in the upper mixed layer and increases the inflow of low-concentration-DIC water from the Southern Yellow Sea (SYS) into the NYS. These ultimately result in the abnormal reduction of DIC concentration in the upper mixed layer of the NYS during the autumn of 2015. This study enriches our understanding of interannual variability of FCO2 in the NYS, which will not only help to further reveal the variations of FCO2 under human activities and climate change, but also provide useful information for guiding the comprehensive assessment of the carbon budget.

Survey on Perkinsus species in two economic mussels (Mytilus coruscus and M. galloprovincialis) along the coast of the East China Sea and the Yellow Sea.

Perkinsus, a parasitic pathogen of marine bivalves, is widely distributed among various mollusks in numerous countries. However, the prevalence and diversity of Perkinsus species in the two economically important mussels, Mytilus coruscus and M. galloprovincialis, in China remain unknown. The presence of the Perkinsus species was identified in the two mussels sampled along the coast of the East China Sea and the Yellow Sea, using both the alternative Ray's fluid thioglycolate medium (ARFTM) and conventional polymerase chain reaction (PCR). The ARFTM test indicated the presence of Perkinsus-like hypnospores in the two mussels. The diameter of the hypnospores in M. coruscus was significantly smaller than that in M. galloprovincialis. The prevalence of Perkinsus in M. galloprovincialis and M. coruscus ranged from 0 to 37.5% and 0 to 25%, respectively. The mean intensity of Perkinsus in M. galloprovincialis and M. coruscus ranged from 0 to 5.14 and 0 to 4.92, respectively. The PCR assay showed that the prevalence of Perkinsus spp. in M. galloprovincialis and M. coruscus was 0 to 25.0% and 0 to 12.5%, respectively. The homology analysis of the newly obtained internal transcribed spacer (ITS) sequences of Perkinsus revealed the highest identity of 100% with P. beihaiensis. The phylogenetic analysis indicated that the Perkinsus isolates from the two mussels were clustered with P. beihaiensis. The results of the molecular biology indicated that only P. beihaiensis was detected in the two mussels. The highest prevalence of P. beihaiensis was observed in Liaoning province (Dalian, 20.83%), followed by Shandong province, Zhejiang province and Fujian province. Consequently, it is recommended that surveillance should be conducted in Dalian, where the prevalence and mean intensity of P. beihaiensis in M. galloprovincialis are the highest.

Outbreaks of Ulva prolifera green tides reduce the network complexity and stability of cooccurring planktonic microbial communities

Ulva prolifera green tides are becoming a worldwide environmental problem, especially in the Yellow Sea, China. However, the effects of the occurrence of Ulva prolifera green tides on the community organization and stability of surrounding microbiomes have still not been determined. Here, the prokaryotic microbial community network stability and assembly characteristics were systematically analyzed and compared between the green tide and non-green tide periods. Ulva prolifera blooms weaken the community complexity and robustness of surrounding microbiomes, increasing fragmentation and decreasing diversity. Bacteria and archaea exhibited distinct community distributions and assembly patterns under the influence of green tides, and bacterial communities were more sensitive to outbreaks of green tides. The bacterial communities exhibited a greater niche breadth and a lower phylogenetic distance during the occurrence of U. prolifera green tides compared to those during the non-green tide period while archaeal communities remained unchanged, suggesting that the bacterial communities underwent stronger homogeneous selection and more sensitive to green tide blooms than the archaeal communities. Piecewise structural equation model analysis revealed that the different responses of major prokaryotic microbial groups, such as Cyanobacteria, to environmental variables during green tides, were influenced by the variations in pH and nitrate during green tides and correlated with the salinity gradient during the non-green tide period. This study elucidates the response of the adaptability, associations, and stability of surrounding microbiomes to outbreaks of U. prolifera green tides.

Methods for detecting green tide in the Yellow Sea using Google Earth Engine platform

The green tide was a blooming phenomenon consisting of outbreaks and aggregations of marine macroalgae, causing severe damage to the ecosystem of the Yellow Sea in China. Remote sensing technology was considered an effective means of green tide detection. Currently, commonly used low spatial resolution satellites cannot satisfy the green tide detection in the fine-scale range. To better improve the accuracy of green tide identification, based on the Commission Internationale del'éclairage (CIE) system, two new green tide detection algorithms (CIE xy, hue angle α) were established using Sentinel-2 MultiSpectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) data on the Google Earth Engine (GEE) platform. These algorithms were validated by introducing the quantitative index Dgw from "Interclass Distance". The CIE xy algorithm and the hue angle α algorithm based on Sentinel-2 MSI exhibited higher Dgw values of 10.29 and 5.87, respectively, surpassing the performance of the two algorithms based on the Landsat-8 OLI in green tide identification (Dgw values of 3.36 and 5.17, respectively). Meanwhile, the proposed algorithms were compared with normalized difference vegetation index (NDVI), difference vegetation index (DVI), ratio vegetation index (RVI), floating algae index (FAI), enhanced vegetation index (EVI), and algal bloom detection index (ABDI) algorithms using Sentinel-2 MSI images. The results showed that the CIE xy algorithm and the NDVI algorithm outperformed the other algorithms. Especially in turbid water, the CIE xy algorithm exhibited significant advantages. However, the hue angle α algorithm still showed some potential in identifying green tide, particularly in the presence of thin cloud interference. These findings can provide effective information support for the monitoring of green tide in the Yellow Sea.

Riverine Influences and Seasonal Dynamics: Exploring Carbonate System Variations and Air‐Sea CO2 Fluxes in the Southern Yellow Sea and East China Sea

AbstractIn this work, the seasonal variations of the carbonate system and air‐sea CO2 fluxes were investigated by two cruises in the southern Yellow Sea (SYS) and the East China Sea (ECS), which were significantly influenced by the Changjiang riverine inputs across seasons. Biological‐mediated change of dissolved inorganic carbon (DIC) was first quantitated through a three end‐member mixing model from winter to spring. Our modeling results suggested that DIC addition through biological remineralization persisted in the SYS during winter and spring, while DIC removal was evident in the Changjiang River Plume and the offshore ECS triggered by river inputs in spring. Horizontal and vertical mixing together constituted the largest contribution to the interseasonal variability of partial pressure of CO2 (pCO2) in the SYS (−69.5%), the river plume (−59.3%), and the ECS offshore (−43.8%), followed by temperature effects in the plume area (29.4%), and biological processes in the ECS offshore (24.1%) and the SYS (−7.7%), with air‐sea CO2 exchange contributing the least in both three subregions. The SYS, the river plume, and the ECS offshore all acted as atmospheric CO2 sinks in both winter and spring. Furthermore, their ability to absorb atmospheric CO2 increased from winter to spring, as reflected in a ∼1.8‐fold increase in the overall spring air‐sea CO2 flux compared to winter estimation.

Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China

The water quality index (WQI) is a widely used tool for comprehensive assessment of river environments. However, its calculation involves numerous water quality parameters, making sample collection and laboratory analysis time-consuming and costly. This study aimed to identify key water parameters and the most reliable prediction models that could provide maximum accuracy using minimal indicators. Water quality from 2020 to 2023 were collected including nine biophysical and chemical indicators in seventeen rivers in Yancheng and Nantong, two coastal cities in Jiangsu Province, China, adjacent to the Yellow Sea. Linear regression and seven machine learning models (Artificial Neural Network (ANN), Self-Organizing Maps (SOM), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB) and Stochastic Gradient Boosting (SGB)) were developed to predict WQI using different groups of input variables based on correlation analysis. The results indicated that water quality improved from 2020 to 2022 but deteriorated in 2023, with inland stations exhibiting better conditions than coastal ones, particularly in terms of turbidity and nutrients. The water environment was comparatively better in Nantong than in Yancheng, with mean WQI values of approximately 55.3–72.0 and 56.4–67.3, respectively. The classifications "Good" and "Medium" accounted for 80 % of the records, with no instances of "Excellent" and 2 % classified as "Bad". The performance of all prediction models, except for SOM, improved with the addition of input variables, achieving R2 values higher than 0.99 in models such as SVM, RF, XGB, and SGB. The most reliable models were RF and XGB with key parameters of total phosphorus (TP), ammonia nitrogen (AN), and dissolved oxygen (DO) (R2 = 0.98 and 0.91 for training and testing phase) for predicting WQI values, and RF using TP and AN (accuracy higher than 85 %) for WQI grades. The prediction accuracy for "Medium" and "Low" water quality grades was highest at 90 %, followed by the "Good" level at 70 %. The model results could contribute to efficient water quality evaluation by identifying key water parameters and facilitating effective water quality management in river basins.

Applying artificial neural network to zero-crossing wave parameters for the wave spectrum

This paper presents a neural network model to estimate wave spectra from wave parameters by the zero-crossing analysis, maximum, significant, and average wave heights and their corresponding wave periods. The present model was trained with the wave data in the JES (Japan/East Sea), and then applied to wave measurements in the Yellow Sea and the South Sea for its feasibility at other seas. According to the present study, the predicted wave spectra well agree with the measurements, even the wave data not in model training. This comparison shows that the accuracy of the present model is comparable to that of JONSWAP wave spectrum. Furthermore, the present model was employed to the wave hindcasting to see its applicability, while JONSWAP wave spectrum also being applied. The significant wave heights from both numerical simulations well agree with the measured ones. However, the average RMSE of significant wave period with the present neural network was 7.08 % smaller than the other, and its Pearson's correlation coefficient is 6.33 % larger on average. From this, it can be seen that the present model was well applied to the wave hindcasting without considering the local wave measurements.

A multi-scale context-aware and batch-independent lightweight network for green tide extraction from SAR images

ABSTRACT The outbreaks of green tide have caused severe harm to the marine environment and human society. Synthetic Aperture Radar (SAR) plays an important role in green tide monitoring by virtue of its high resolution and cloud-free nature. The existing green tide extraction methods still face challenges in identifying multi-scale green tide patches due to noise interference, uneven greyscale and blurred boundaries in SAR images. Meanwhile, the practical application of deep learning methods with high precision is limited due to the complexity of the model and the large amount of computation. Therefore, we propose a multi-scale context-aware and batch-independent lightweight green tide extraction network called MBL-Net. A novel lightweight heterogeneous backbone is designed to extract multi-scale discriminative features and improve segmentation efficiency by using multi-scale selection kernel (MSK) modules and lightweight stages. Meanwhile, Triplet attention module is introduced to improve the internal consistency of the green tide region and suppress the effect of speckle noise. Then, the mixed pooling-based channel prior module (MCPM) is used to expand the receptive field of the network and extract the fine green tide structure by fusing multi-scale features. In addition, Filter Response Normalisation (FRN) is innovatively applied for feature normalization in the decoding stage, eliminating batch dependency. In order to verify the effectiveness of the proposed method, a dataset is built using the Sentinel-1 images of the Yellow Sea, China, from 2019 to 2021. The experimental results show that the proposed method achieves an overall accuracy of 98.59% with 0.970 G FLOPs and 3.525 M parameters, which ensures high precision and improves green tide detection efficiency. Compared with several representative networks, this method can capture more details of green tide with fewer parameters and faster calculation speed.

Deciphering and quantifying nitrate sources and processes in the central Yellow Sea using dual isotopes of nitrate

Anthropogenic activities pose significant challenges to the accumulation of coastal nitrogen (N). Accurate identification of nitrate (NO3−) sources is thus essential for mitigating excessive N in many marginal seas. We investigated the dual isotopes of NO3− in the central Yellow Sea to elucidate the sources and cycling processes of NO3−. The results revealed significant spatial variability in NO3− concentrations among the Yellow Sea Surface Water (YSSW), Changjiang Diluted Water (CDW), Yellow Sea Cold Water Mass (YSCWM), and Taiwan Warm Current Water (TWCW). Stratification played a crucial role in restricting vertical nutrient transport, leading to distinct nutrient sources and concentrations in different water masses. The dual NO3− isotopic signature indicated that atmospheric deposition was the primary source of surface NO3−, contributing approximately 30 % to the NO3− in the YSSW. In the NO3−-rich CDW, the heavier δ15N-NO3− and δ18O-NO3− suggested incomplete NO3− assimilation. Organic matter mineralization and water stratification played crucial roles in the accumulation of nutrients within the YSCWM and TWCW. Notably, regenerated NO3− accounted for approximately half of the NO3− stored in the YSCWM. A synthesis of NO3− dual isotope data across the coastal China seas revealed significant spatial and seasonal variations in the N source. The study emphasized the dynamics of coastal NO3− supply, which are shaped by the complex interconnections among marine, terrestrial, and atmospheric processes. Our approach is a feasible method for exploring the origins of N amidst the escalating pressures of anthropogenic nutrient pollution in coastal waters.

Response of Sediment Dynamics to Tropical Cyclones under Various Scenarios in the Jiangsu Coast

The Jiangsu Coast (JC), China, is an area susceptible to the impact of tropical cyclones (TCs). However, due to the lack of available on-site observation data, nearshore sedimentary dynamic processes under the impact of TCs have not been fully explored. This study developed a 3D wave–current–sediment numerical model for the JC based on the Finite Volume Community Ocean Model (FVCOM) to investigate sediment dynamic responses to TCs under various scenarios, including different tracks, intensities of TCs and tidal conditions. The validation results demonstrated the model’s satisfactory performance. According to the simulation results, typhoons can significantly impact the hydrodynamics and sediment dynamics. During Typhoon Lekima in 2019, strong southeasterly winds substantially increased the current velocity, bottom stress, wave height, and suspended sediment concentration (SSC). Three typical landfall-type typhoons, with prevailing southeasterly winds, brought significant sediment flux from southeast to northwest along the coast, while the typhoon that moved northward in the Yellow Sea induced a relatively small sediment flux from north to south. Typhoons could also induce stripe-like erosion and deposition, which is closely related to seafloor topography, resulting in seabed thickness variations of up to ±0.3 m. Additionally, strengthening typhoon wind fields can lead to increased sediment flux and seabed morphological changes. Typhoon Winnie, particularly at spring tide, had a greater impact on sediment dynamics compared to other landfall typhoons. Numerical simulations showed that the typhoon-induced net sediment flux within the spring tidal cycle could increase by 80% to 100% compared to the neap tidal cycle, indicating the significant influence of tidal conditions on sediment transport during TC events.