Nearshore water-level prediction has a substantial impact on the daily lives of coastal residents, fishing operations, and disaster prevention and mitigation. Compared to the limitations and high costs of traditional empirical forecasts and numerical models for nearshore water-level prediction, data-driven artificial intelligence methods can more efficiently predict water levels. Attention mechanisms have recently shown great potential in natural language processing and video prediction. Convolutional long short-term memory(ConvLSTM) combines the advantages of convolutional neural networks (CNN) and long short-term Memory (LSTM), enabling more effective data feature extraction. Therefore, this study proposes a ConvLSTM nearshore water level prediction model that incorporates an attention mechanism. The ConvLSTM model extracts multiscale information from historical water levels, and the attention mechanism enhances the importance of key features, thereby improving the prediction accuracy and timeliness. The model structure was determined through experiments and relevant previous studies using five years of water level data from the Zhuhai Tide Station and the surrounding wind speed and rainfall data for training and evaluation. The results indicate that this model outperforms the four other baseline models of PCCs, RMSE, and MAE, effectively predicting future water levels at nearshore stations up to 48 h in advance. Compared to the ConvLSTM model, the model with the attention mechanism showed an average improvement of approximately 10% on the test set, with a greater error reduction in short-term forecasts than that in long-term forecasts. During Typhoon Higos, the model reduced the MAE of the best-performing baseline model by approximately 3.2 and 2.4 cm for the 6- and 24-hour forecasts, respectively, decreasing forecast errors by approximately 18% and 11%, effectively enhancing the ability of the model to forecast storm surges. This method provides a new approach for forecasting nearshore tides and storm surges.

Microorganisms, especially rare microbial species, are crucial in estuarine ecosystems for driving biogeochemical processes and preserving biodiversity. However, the understanding of the links between ecosystem multifunctionality (EMF) and the diversity of rare bacterial taxa in estuary ecosystems remains limited. Employing high-throughput sequencing and a variety of statistical methods, we assessed the diversities and assembly process of abundant and rare bacterioplankton and their contributions to EMF in a subtropical estuary. Taxonomic analysis revealed Proteobacteria as the predominant phylum among both abundant and rare bacterial taxa. Notably, rare taxa demonstrated significantly higher taxonomic diversity and a larger species pool than abundant taxa. Additionally, our findings highlighted that deterministic assembly processes predominantly shape microbial communities, with heterogeneous selection exerting a stronger influence on rare taxa. Further analysis reveals that rare bacterial beta-diversity significantly impacts to EMF, whereas alpha diversity did not. The partial least squares path modeling (PLS-PM) analysis demonstrated that the beta diversity of abundant and rare taxa, as the main biotic factor, directly affected EMF, while temperature and total organic carbon (TOC) were additional key factors to determine the relationship between beta diversity and EMF. These findings advance our understanding of the distribution features and ecological knowledge of the abundant and rare taxa in EMF in subtropical estuaries, and provide a reference for exploring the multifunctionality of different biospheres in aquatic environments.

In the context of global climate change and rising sea levels, the adverse impacts of storm surges on the environment, economy, and society of affected areas are becoming increasingly significant. However, due to differences in geography, climate, and other conditions among the affected areas, a single method for assessing the risk of storm surge disasters cannot be fully applicable to all regions. To address this issue, an increasing number of new methods and models are being applied in the field of storm surge disaster risk assessment. This paper introduces representative traditional statistical methods, numerical simulation methods, and artificial intelligence-based techniques in this field. It compares these assessment methods in terms of accuracy, interpretability, and implementation difficulty. The paper emphasizes the importance of selecting appropriate assessment methods based on specific conditions and scientifically combining various methods in practice to improve the accuracy and reliability of storm surge disaster risk assessments.

In this study, the spatial distribution and trends of wind energy (as measured by wind and wind power density) were investigated from 1979 to 2021 across various time scales over the South China Sea (SCS)by utilizing ERA5 reanalysis data. The results indicate that the SCS possesses abundant wind energy. In addition, due to the fact that the East Asian monsoon dominates the SCS, the wind energy exhibits obvious seasonal changes. It is in winter and autumn that the winter monsoon (i.e., the northeast wind) prevails over the SCS. Here, the wind energy is abundant and reaches its maximum in December. In summer, the summer monsoon (i.e., the southwest wind) prevails over the SCS. Here, the wind energy is abundant over the southwestern SCS. In spring, however, the wind energy is poor. The annual mean wind energy shows a decreasing trend along the northern coast and an increasing trend over the central SCS. The trends of seasonal mean wind energy in winter, spring, and summer demonstrate a similar pattern to the annual mean wind energy. With respect to the intensity of the trends, they are strongest in winter, followed by spring and autumn, and weakest in summer. The trend of wind energy in autumn almost demonstrates the opposite pattern in comparison with the other seasons, i.e., both decreasing and increasing trends over the northern and southern SCS, respectively. The decreasing trend of wind energy along the northern coast of the SCS occurs in February, April, July, September, and November, whereas the increasing trend over the central SCS appears from the period of December to June. The spatial distribution and trends of wind energy over the SCS can help with issuing a more informed recommendation with respect to offshore wind energy planning.

Annually, hundreds of individuals tragically lose their lives at sea due to shipwrecks or aircraft accidents. For search and rescue personnel, the task of locating the debris of a downed aircraft in the vastness of the ocean presents a formidable challenge. A primary task these teams face is determining the search area, which is a critical step in the rescue operation. The movement of aircraft wreckage on the ocean surface is extremely complex, influenced by the combined effects of surface winds, waves, and currents. Establishing an appropriate drift motion prediction model is instrumental in accurately determining the search area for the wreckage. This article initially conducts maritime drift observation experiments on wreckage, and based on the results of these experiments, analyzes the drift characteristics and patterns of the debris. Subsequently, employing a wealth of observational experimental data, three types of drift prediction models for the wreckage are established using the least squares method. These models include the AP98 model, the dynamics model, and an improved model. In conclusion, the effectiveness and accuracy of the three models is evaluated and analyzed using Monte Carlo techniques. The results indicate that the probability of positive crosswind leeway (CWL) is 47.4%, while the probability of negative crosswind leeway (CWL) is 52.6%. The jibing frequency is 7.7% per hour, and the maximum leeway divergence angle observed is 40.4 degrees. Among the three drift prediction models, the refined AP98 drift model demonstrates the highest forecasting precision. The findings of this study offer a more accurate drift prediction model for the search of an aircraft lost at sea. These results hold significant guiding importance for maritime search and rescue operations in the South China Sea.