Based on the non-hydrostatic numerical calculation model, this paper systematically studies the wave dissipation characteristics of permeable submerged breakwater under the impact of focused wave. By setting reasonable calculation conditions, the effects of wave height, water depth above the submerged breakwater, spectral peak period, porosity and the crest width of submerged breakwater on the wave dissipation characteristics of permeable submerged breakwater are analyzed in detail. At the same time, the calculation results of permeable submerged breakwater are compared with those of impermeable submerged breakwater. The calculation results show that the attenuation effect of permeable submerged breakwater on focused wave is stronger than that of impermeable submerged breakwater, which shows that permeable submerged breakwater can more effectively reduce the impact of freak wave on coastal infrastructure; wave height and the water depth above the submerged breakwater are important factors affecting the wave dissipation characteristics of submerged breakwater. With the increase of incident wave height and the decrease of the water depth above the submerged breakwater, the wave dissipation effect of permeable submerged breakwater increases gradually. The permeable submerged breakwater has poor wave dissipation effect on large-spectrum peak period waves. Within the range of porosity considered in this paper, as the porosity increases, the wave dissipation effect of permeable submerged breakwater is better; when the porosity is 0.4 and the crest width is 0.6125 m, the permeable submerged breakwater can reduce 54% of the incident wave energy, which is 36.1% higher than that of the impermeable submerged breakwater. The research results of this paper can provide corresponding reference for further understanding the wave dissipation characteristics of permeable submerged breakwater and the design of coastal protection engineering.

There are few researches on offshore gusts at home and abroad, and most of them focus on gust prediction and application research. There is no systematic discussion on the acquisition technology of wind gust data. Based on the backscattering coefficient observed by HY-2B satellite radar altimeter and the brightness temperature information observed by correction microwave radiometer, a method for retrieving gust wind speed is proposed in this paper. The gust wind speed obtained from the joint inversion of the two remote sensing sensors is verified with the National Data Buoy Center (NDBC) buoy data from 2019 to 2021. The results show that the gust wind speed root mean square error (RMSE) is 0.98 m/s and the correlation coefficient is 0.82. The RMSE of the gust wind speed obtained based on the method using a similar satellite Jason-3 is 0.96 m/s and the correlation coefficient is 0.88. Based on the observation of sea surface wind speed with HY-2B satellite radar altimeter and the synchronous observation information of correction microwave radiometer by satellite platform, the observation of sea surface wind gust is realized jointly. The comparison results of data show that the method in this paper has high observation accuracy. At the same time, this method is also applicable to domestic and foreign satellites with the same observation system. This provides a simple and reliable means of ocean remote sensing technology for the current situation of insufficient observation capacity of offshore wind gust.

The assimilation fusion or interpolation fusion of the sea surface wind field based on multi-source data is currently restricted by computing power. This paper proposes to train the XGBoost-based machine learning ERA-5 data correction fusion model in the overlapping area of the multi-source satellite data and the ERA-5 reanalysis data, and then use the model to quickly correct (machine learning inference) ERA-5 data, of which the ERA-5 whole area correction fusion it only takes about 2 seconds. Due to the rapidity of machine learning inference, the entire sea surface fusion wind field can be constructed at a lower computational cost. This paper expands on typical wind field variables such as 10 m wind speed, 10 m wind direction, U10 component and V10 component, taking into account the difference in sea and land distribution, using land masks to eliminate land areas, and constructing D_S_A_XGBoost, D_S_O_XGBoost, U_V_A_XGBoost, U_V_O_XGBoost corrections model, and finally generate sea surface fusion wind field. By comparing the ERA-5 reanalysis data before and after the correction with the satellite data, the above four models all reduce the gap between the ERA-5 reanalysis data and the satellite data. Especially in terms of wind speed, both root mean square error (RMSE) and mean absolute error (MAE) are effectively reduced. In terms of wind direction, RMSE_d and MAE_d also show a decreasing trend. Using Tropical Atmosphere Ocean Array (TAO) buoy data to evaluate the four XGBoost models, it is found that the U_V_O_XGBoost model has the best correction results for ERA-5 data, and its correlation reaches 0.893, an increase of about 0.011, and the results show that the fusion speed is greatly improved under the condition of ensuring the accuracy of wind field.

Retrieving shallow water depth based on multispectral satellite imagery is highly cost-effective. However, the extensive application of satellite-derived bathymetry has been restricted by its low prediction accuracy. To improve about the accuracy of the retrieved bathymetry, spatial autocorrelation features within the in situ depth measurements and the multi-spectral image are focused in this research. To this end, we develop a machine learning method combining with spatial autocorrelation features and statistical intercorrelation features of learned samples. The experimental results of Xisha Beidao show that compared with the traditional machine learning, the accuracy of the new method is improved by 18% when the number of in situ depths is small. On the contrary, when the number of in situ depths is large, an improvement of 27% in root mean square error is achieved. This demonstrates that incorporating the spatial autocorrelation features of data sources into the machine learning can significantly improve the prediction accuracy, and then provide effective data support for shallow ocean research.

Global navigation satellite system-reflectometry (GNSS-R) technology is an emerging technology for monitoring sea level changes. Based on the principle of the signal to noise ratio (SNR) analysis method in GNSS-R technology, this paper established a new sea level height estimation model to improve the accuracy by analyzing the process of separating the trend term and extracting the oscillation frequency. Aiming at the problem of poor signal separation in the traditional model, this paper proposed to use the variational mode decomposition (VMD) algorithm to replace the traditional least squares fitting (LSF) to separate the trend term components. On this basis, this paper combined Lomb-Scargle Periodogram (LSP) spectral analysis method and Kaiser window function (referred to as WinLSP) to reduce the inversion error caused by spectral leakage. The results of sea level inversion experiments carried out at GTGU Station in Onsala, Sweden and SC02 Station in Alaska, USA show that the estimation model established in this paper has higher inversion accuracy than traditional model. The root mean square error (RMSE), correlation coefficient and number of inversion points of the inversion results of GTGU Station based on the VMD+WinLSP estimation model are 4.70 cm, 0.98 and 5 647, respectively. The inversion accuracy and GNSS data utilization are increased by about 29.7% and 15.0%, respectively; The RMSE, correlation coefficient, and inversion points of SC02 Station are14.34 cm, 0.99 and 1 785, respectively, and the inversion accuracy and GNSS data utilization are increased by about 12.3 % and 9.4%.

Increasing heat stress due to global warming is the main threat to coral reef regions over the South China Sea islands. Coral reefs bleaching events are most often predicted by heat stress, which will benefit the protection and management coral reefs. Degree heating week (DHW) is used to measure the intensity and duration of heat stress experienced on coral reefs, represents the accumulation of positive sea surface temperature (SST) anomaly at that location over the past 12 week periods. This study utilizes the National Oceanic and Atmospheric Administration-Coral Reef Watch (NOAA-CRW) SST dataset to investigate spatio-temporal in the heat stress of the coral reef regions of the South China Sea islands between 1985 to 2019 and its relevance to El Niño. K-means cluster analysis was performed on the 35-year maximum degree heating week values per pixel, and the coral reefs of the South China Sea islands were divided into 6 regions: Nansha−1, Nansha−2, Nansha−3, Dongsha, Xisha and Zhongsha coral reef region. The main results are as following: (1) The maximum DHW of the coral reef regions of the South China Sea islands is 0−12.9°C-week, and it decreases from high to low in latitude. (2) The linear fitting method was used to analyze the annual maximum DHW from 1985 to 2019. The results showed that the thermal pressure intensity in the coral reef area of the South China Sea islands showed an upward trend, ranging from 0.013°C to 0.174°C per week. The maximum DHW in the coral reef area of the South China Sea islands appeared in 1998, 2010, 2014. (3) The maximum annual DHW might have caused 93.9% of coral reefs to have more than one bleaching risk event, and 19.6% of coral reefs to have at least one risk of death. (4) The cross-wavelet analysis of monthly mean DHW in the coral reef regions of the South China Sea islands and Oceanic Niño index shows that there are time-frequency characteristics and time-lag correlation of multi-period 8−32 months resonance period, which confirms that the thermal pressure of coral reefs in the South China Sea islands increases significantly with the occurrence of El Niño events. The time lag correlation analysis shows that Oceanic Niño index is positively correlated with the thermal pressure in the coral reef regions of the South China Sea islands, and the latter lags behind the former by 7−9 months.

Estuarine flow is the fundamental driving of physical processes such as estuarine ecological environment, river evolution and material transport. Due to the runoff, tide, topography and meteorology, the estuarine flow presents a complex three-dimensional structure, including not only the residual flow induced by fresh water injection, but also the periodical tidal current, wind-driven flow, baroclinic flow and the residual flow caused by estuarine nonlinearity. In order to explore the composition and intra-tidal variation of estuarine flow, based on the field data in the Oujiang River Estuary, the estuarine flow is decomposed by principal component analysis (PCA) method, and the decomposition performance of PCA method for estuarine flow and high-frequency characteristics of baroclinic overtide are deeply discussed. The results show that PCA method can operate original data or standardized data in the study of estuarine flow structure. PCA method can decompose baroclinic components (estuarine gravitational circulation structure), but cannot separate barotropic components (runoff and tidal current). The coupled effect of runoff and tidal current is reflected in the score of principal components. The filter of principal components should employ conjoint judgment of flow structure and cumulative interpretation variance, rather than only cumulative interpretation variance. The estuarine baroclinic flow has clearly high-frequency characteristics, which approximates quarter-diurnal.

Driven by the reanalysis wind data NCEP CFSV2, the third-generation wave model SWAN is utilized in a self-nested grid system to simulate waves in the Beibu Gulf (BG), South China Sea (SCS) for a period of one year. The model accuracy is examined by comparing the numerical results with the Jason-2 satellite altimetry data and the near-shore buoy measurements. Based on the numerical simulations, the influence of spatial resolutions on model predication is evaluated, the seasonal characteristics of waves in the BG are analyzed, and the forcing contributions of local wind in BG and sea waves from SCS are discussed. The results show that: (1) Compared with the Jason-2 satellite data, the root-mean square bias (RMSB) and scatter index (SI) for significant wave height are approximately 0.4 m and 0.2, respectively. Compared with the near-shore buoy observations, the RMSB and SI for significant wave height are about 0.2 m and 0.4, respectively; the RMSB and SI for mean wave period are roughly 0.6 s and 0.2, respectively; and the RMSB for mean wave direction is around 30°. (2) The numerical model with the spatial resolution 12'×12' can predict reasonable results for the open sea area of BG, and the mean relative bias compared to the model results of 2'×2' is not exceeding 10%. (3) In the BG, the northeasterly waves prevail in winter monsoon, the southerly waves reign in summer monsoon, and the southeast waves predominate during the periods of monsoon transition (MT). Waves are stronger in monsoons than MTs, up to the strongest in winter monsoon and down to the weakest when winter goes to summer. (4) The driving contribution of local wind to waves in the BG increases gradually from the bay mouth to the inner bay, and the contribution is stronger in monsoons than MTs. The driving contribution of sea waves from SCS gradually weakens from the bay mouth to the inner bay, and the contribution is weaker during monsoons than MTs. In the middle and northern parts of BG, waves are mainly controlled by local wind. In the water areas to the south and east of Hainan Island, waves are mainly dominated by sea waves from SCS. While in the areas to the southwest of Hainan Island, waves are jointly affected by the both factors.

Aiming at two types of typhoon field parameters used in parametric typhoon wind field model, such as radius to maximum wind speed ${R}_{{\rm{max}}}$ and radial pressure distribution coefficient $(B)$, the process of two typhoon was reproduced with different combinations of parameters ${R}_{{\rm{max}}}$ and $ B $, and compared with the field observation data of three observation sites. The constructed typhoon wind field was used as the driving wind field in the MIKE 21 model, which was used to complete numerical simulation of the storm surge of two typhoons in Zhejiang Province. The tide level and storm tide level were verified and analyzed with the observation data. The results show that the parametric model selected in this paper is suitable for calculating typhoon wind field which affect the coast of Zhejiang, and the simulation results of storm surge model established on this basis meet the requirement of accuracy.

Based on the Earth System Model (ESM2M) of the Coupled Model Intercomparison Project 5 (CMIP5), combined with Argo observation data and the reanalysis dataset compiled by Ishii et al., this paper presents the seasonal variation characteristics of mixed layer depth (MLD) and subduction process in the subtropical Northeast Pacific Ocean (10°−40°N, 110°−160°W) under the present climate background and extreme enhancement of radiative forcing are presented, to study its response to global warming. Under the current climate background, both of MLD and subduction rate reach their maximum values in winter. The main contribution items of subduction rate have significant seasonal variation. From January to May, the subduction rate is mainly controlled by the change of lateral induction rate, while from June to December, the main mechanism is the change of Ekman pumping velocity controlled by wind stress curl. After global warming, the main control elements of seasonal signals remain unchanged. However, under the influences of wind stress curl and other factors, the MLD in each season decreased and the range of the core maximum region shrinks. As the decrease in winter is much larger than that in summer, the seasonal fluctuation range (amplitude) of MLD is significantly smaller. In the long run, MLD shows a trend of continuous shallower, and the weakening of MLD front caused by the weakening of its spatial non-uniformity is the key to control the weakening of lateral induction rate and eventually lead to the weakening of the subduction rate. Since the seasonal variation signal of Ekman pumping velocity has little response to global warming, subduction rate is most strongly affected in winter. The results show that the contribution proportion of the two key factors to the subduction rate changes with the seasons: when the MLD front is strong in winter, the influence of the lateral induction rate will be significantly enhanced. The different variations of the two factors before and after global warming will significantly change the seasonal amplitude of the subduction rate, which may have a profound impact on the formation and transport of mode water in the region.