Studying the sea–air interaction between the upper ocean and typhoons is crucial to improve our understanding of heat and momentum exchange between the atmosphere and the ocean. There is a strong heat flux exchange between the atmosphere and the ocean during the impact of a typhoon, and the physical fields, such as the wind field, wave field, flow field, and SST field, also interact with each other. A fully coupled Atmosphere–Wave–Ocean model in the South China Sea was established by the mesoscale atmospheric model WRF, wave model SWAN, and the regional ocean model ROMS based on the COAWST model system. Typhoon Kai-tak was simulated using this fully coupled model and some other coupled schemes. In this paper, the variation of sea surface temperature (SST) and ocean subsurface temperature caused by Typhoon Kai-tak is analyzed by the fully coupled model, and the basic characteristics of the response of the upper ocean to the typhoon are given. The simulation results demonstrate that the fully coupled WRF-SWAN-ROMS model shows that the typhoon passes through the sea with obvious cooling. In the cold eddy region, the sea surface temperature cools 4 to 5 °C, and the cooling zone is concentrated on the right side of the track. The change of sea surface temperature lags more than 12 h behind the change of sea surface height. The decrease of SST on the left side of the track was relatively small: ranging from 1.5 to 2.5 °C. The disturbance of typhoon causes the subsurface water to surge to the surface, changes the temperature distribution of the surface, and causes the mixing layer to deepen about 40 m to 60 m. The simulation results reveal the temporal and spatial distribution of sea temperature and mixed layer depth. The sea surface temperature field has an asymmetrical distribution in space and has a lag in time. The heat exchange at the air–sea interface is very strong under the influence of the typhoon. The heat exchange between the air and sea is divided into latent heat and sensible heat, and the latent heat generated by water vapor evaporation plays a dominant role in the heat exchange at the air–sea interface, which shows that the heat carried by the vaporization of the sea surface is one of the important factors for the decrease of sea temperature under the influence of the typhoon.

We present a study on regular wave propagation on a sloping bed under the action of steady wind, which is of a great significance to complement and replenish the interaction mechanisms of nearshore wave and wind. Physical experiments were conducted in a wind-wave flume, and the corresponding numerical model was constructed based on the solver Waves2FOAM in OpenFOAM, with large-eddy simulation (LES) used to investigate the turbulent flow. The comparisons between the measured and calculated results of the free surface elevation and flow velocity indicated that the numerical model could predict the associated hydrodynamic characteristics of a nearshore wave regardless of the presence or absence of wind. The results showed that wind had a significant impact on nearshore wave evolution. It was found that under the same wind speed coverage constraint, wave breaking occurred ahead of time. The smaller the surf similarity ${\xi }_0$ was, the higher the dispersion degree of wave breaking locations would be, and the breaker index of $H_b/h_b$ increased with wind speed under the same incident wave height. The main components of analysis for turbulent flow were the results of the cross-spectrum, the TKE (turbulent kinetic energy), and TDR (turbulent dissipation rate). The cross-spectrum illustrated that wind enhanced the degree of coherence of the residual velocity components and aggravated turbulence. The TKE indicated that in regions near the water surface, wind speed made it considerably larger and the average level rapidly decreased with depth. The TDR exhibited that the significant effect of wind was merely imposed after breaking, wherein the turbulence penetrated the deeper flow and the average level generally rose. The velocity profile on the slope showed that the wind accelerated the undertow, and the moment statistics indicated that the velocity distribution deviated gradually from the Gaussian distribution to the right.

Typhoons are major marine dynamic disasters that affect the coastal ocean areas of China. During a typhoon, the coupling dynamic factors, such as wind, waves, storm surges, and river runoff, greatly enhance the mass and energy exchange at the various interfaces of the ocean. A fully coupled atmosphere-wave-ocean model in the South China Sea (SCS) was established based on the WRF, SWAN, and ROMS models. The variation of sea surface salinity (SSS) and ocean subsurface salinity caused by Typhoon Kai-tak (201213) was analyzed by the fully coupled model, and the basic characteristics of the response of the upper ocean to the typhoon are given in this paper. The simulation results demonstrate that the salinity of the sea surface showed a sharp change during Typhoon Kai-tak, and it changed gradually after entering the recovery period. During the passage of Typhoon Kai-tak, the disturbance caused by strong winds strengthened the mixing process of the water in the Pearl River Estuary (PRE) and its adjacent waters. As the typhoon developed, under the influence of Ekman pumping, the mixing effect between the subsurface and the bottom and the upper water was obvious. Before the impact of Typhoon Kai-tak, the salinity had obvious stratification characteristics along the water depth. Due to the influence of the storm surge, the surface water with increased salinity was transported to the estuary, which led to an increase in the salinity of the estuary’s surface water. In this condition, it is highly likely for there to be saltwater intrusion. The salinity distribution characteristics of three schemes (ROMS model only, coupled WRF-ROMS model, and fully coupled WRF-SWAN-ROMS model) were compared in this study. In the fully coupled WRF-SWAN-ROMS model, the disturbance of the bottom water was the most obvious, and the salinity value was greater than that of the coupled WRF-ROMS model, which indicates that under the influence of waves, the mixing and exchange abilities were strengthened.

Under the action of gravity, buoyancy, and surface tension, bubbles generated by wave breaking will rupture and polymerize, causing the occurrence of high-speed jets and strong turbulence in nearby water bodies, which in turn affects sea–air exchange, sediment transport, and pollutant movement. These interactions are closely related to the shape and velocity changes in single bubbles. Therefore, understanding the motion characteristics of single bubbles is essential. In this research, a large number of experiments were carried out to serve this purpose. The experimental data were used to develop three machine learning models for the bubble final velocity, bubble drag coefficient, and bubble shape, respectively. The performance of the feed forward back propagation neural network (FBNN) models for the final velocity and drag coefficient were evaluated. The coefficient of determination (R²) and root mean squared error (RMSE) value of final velocity prediction model was recorded at 0.83 and 0.0518, respectively. Meanwhile, for the drag coefficient prediction model, the values are 0.92 for R² and 0.1534 for RMSE. The models can provide a more accurate output if compared to that from the empirical formulas. K-nearest neighbours (KNN), logistic regression, and random forest were applied as the algorithm while developing the bubble shape classification model. The best performance is achieved by the logistic regression.

During the past few decades, the ecosystems of lakes have been reshaped greatly by global climate change and expanding human activities. As the second largest freshwater lake in China, Dongting Lake is the most important regulating lake in the Yangtze River Basin because it has extensive flood storage capacity. The dynamic characteristics of its circulation and sediment transport are significantly affected by the scheduling and interception of control reservoirs at the upper reaches of the Yangtze River. In this paper, a central lake reservoir is proposed to improve the flood and drought resistance of Dongting Lake. The efficiency of the central lake reservoir is investigated numerically by developing a two-dimensional shallow water model. We demonstrate that current velocity and water elevation during flood and drought events can be influenced significantly by the construction of the central lake reservoir. The flood storage capacity of the central lake reservoir can reduce the peak flood elevation significantly in West Dongting Lake, which would enhance its flood resistance. The water replenishment of the central lake reservoir in the dry season can also efficiently increase the lake water elevation to enhance the drought resistance in the area surrounding the lake. Our findings have important implications for policy makers and their management of Dongting Lake.

The Tongtian River is the source of the Yangtze River and is a national key ecological reserve in China. Monitoring and predicting the changes and mechanisms of the Tongtian River channel morphology are beneficial to protecting the “Asian Water Tower”. This study aims to quantitatively monitor and predict the accretion and erosion area of the Tongtian River channel morphology during the past 30 years (1990–2020). Firstly, the water bodies of the Tongtian River were extracted and the accretion and erosion areas were quantified using 1108 Landsat images based on the combined method of three water-body indices and a threshold, and the surface-water dataset provided by the European Commission Joint Research Centre. Secondly, an intelligent lightweight neural-network model was constructed to predict and analyze the accretion and erosion area of the Tongtian River. Results indicate that the Tongtian River experienced apparent accretion and erosion with a total area of 98.3 and 94.9 km², respectively, during 1990–2020. The braided (meandering) reaches at the upper (lower) Tongtian River exhibit an overall trend of accretion (erosion). The Tongtian River channel morphology was determined by the synergistic effect of sediment-transport velocity and streamflow. The lightweight neural network well-reproduced the complex nonlinear processes in the river-channel morphology with a final prediction error of 0.0048 km² for the training session and 4.6 km² for the test session. Results in this study provide more effective, reasonable, and scientific decision-making aids for monitoring, protecting, understanding, and mining the evolution characteristics of rivers, especially the complex change processes of braided river channels in alpine regions and developing countries.