Search Results (38)

Abnormal tidal levels pose a serious threat to maritime navigation, coastal infrastructure, and human life and property. Therefore, it is crucial to accurately predict tidal levels. However, due to the influence of topography and meteorology, tidal levels exhibit complex and non-stationary characteristics, making high-precision prediction a significant challenge. This study proposes a tidal prediction model, named SVMD-BiLSTM-Residual Decomposition (SBRD), which combines Successive Variational Mode Decomposition (SVMD) and Bidirectional Long Short-Term Memory (BiLSTM) networks. SBRD decomposes non-stationary tidal signals into simpler intrinsic mode functions (IMFs) using SVMD. Each IMF is then independently predicted using a BiLSTM network, and the final prediction is obtained through signal reconstruction. Experimental results show that SBRD accurately predicts tidal levels within a 24 h horizon and maintains robust performance during abnormal tidal events, such as acqua alta. Compared to other models, SBRD achieves the highest prediction accuracy and the lowest error, with a Coefficient of Determination (R²) exceeding 99%, a Mean Absolute Error (MAE) of 1.33 cm or less, and a Root Mean Square Error (RMSE) within 2.13 cm for tidal forecasts within a 24 h horizon. These results demonstrate that SBRD effectively enhances the accuracy of tidal level prediction, contributing to the advancement of marine economic technologies and the prevention and mitigation of marine disasters. Full article

The Ganges–Brahmaputra–Meghna (GBM) delta, characterized by complex topography and hydrological conditions, is highly susceptible to recurrent flooding, particularly in its coastal regions where tidal dynamics hinder floodwater discharge. This study integrates Synthetic Aperture Radar (SAR) imagery with machine learning (ML) techniques to assess near real-time flood inundation patterns associated with extreme weather events, including recent cyclones between 2017 to 2024 (namely, Mora, Titli, Fani, Amphan, Yaas, Sitrang, Midhili, and Remal) as well as intense monsoonal rainfall during the same period, across a large spatial scale, to support disaster risk management efforts. Three machine learning algorithms, namely, random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN), were applied to flood extent data derived from SAR imagery to enhance flood detection accuracy. Among these, the SVM algorithm demonstrated the highest classification accuracy (75%) and exhibited superior robustness in delineating flood-affected areas. The analysis reveals that both cyclone intensity and rainfall magnitude significantly influence flood extent, with the western coastal zone (e.g., Morrelganj and Kaliganj) being most consistently affected. The peak inundation extent was observed during the 2023 monsoon (10,333 sq. km), while interannual variability in rainfall intensity directly influenced the spatial extent of flood-affected zones. In parallel, eight major cyclones, including Amphan (2020) and Remal (2024), triggered substantial flooding, with the most severe inundation recorded during Cyclone Remal with an area of 9243 sq. km. Morrelganj and Chakaria were consistently identified as flood hotspots during both monsoonal and cyclonic events. Comparative analysis indicates that cyclones result in larger areas with low-level inundation (19,085 sq. km) compared to monsoons (13,829 sq. km). However, monsoon events result in a larger area impacted by frequent inundation, underscoring the critical role of rainfall intensity. These findings underscore the utility of SAR-ML integration in operational flood monitoring and highlight the urgent need for localized, event-specific flood risk management strategies to enhance flood resilience in the GBM delta. Full article

The prediction of tidal fields is crucial in coastal and marine hydrodynamic analyses, particularly in complex tidal environments, as it plays an essential role in disaster warning and fisheries management. However, monitoring the entire tidal field is impractical, and harmonic analysis and numerical simulation methods continue to face challenges in accuracy and efficiency for large-scale predictions. To address these issues, this paper proposes a tidal field prediction method based on Long Short-Term Memory (LSTM) networks. A physics-based hydrodynamic model is established, and the numerical model is validated using observational data from multiple sites in the study area. The accuracy is quantified using performance indicators such as root mean square error (RMSE) and correlation coefficients. The validated numerical model is then used to generate a high-quality comprehensive dataset. An LSTM-based model is then developed to predict tidal fields in a semi-closed marine bay. The performance of the LSTM-based model is compared with models developed using Transformer, Random Forest, and KNN regression methods. The results demonstrate that the LSTM-based model surpasses the other machine learning models in prediction accuracy, with a notable advantage in handling time series field data. This study introduces new ideas and technical approaches for rapid tidal field prediction, overcoming the limitations of traditional methods and providing robust support for coastal disaster prevention, resource management, and environmental protection. Full article

The recurring occurrence of green tides as an ecological disaster has been reported annually in the Yellow Sea. While remote sensing technology effectively tracks the scale, extent, and duration of green tide outbreaks, there is limited research on the underlying driving mechanisms of green tide drift transport and the determination of the leeway coefficient. This study investigates the green tide transport mechanism and evaluates the feasibility of estimating the leeway coefficient by analyzing green tide drift velocities obtained from Geostationary Ocean Color Imager-II (GOCI-II) images using the maximum cross-correlation (MCC) technique and leeway method across various time intervals alongside ocean current and wind speed data. The results reveal the following: (1) Significant spatial variations in green tide movement, with a distinct boundary at 34°40′N. (2) Short-term green tide transport is primarily influenced by tidal forces, while wind and ocean currents, especially the combined Ekman and geostrophic current component, predominantly govern net transport. (3) Compared to 1, 3, and 7 h intervals, estimating the leeway coefficient with a 25 h interval is feasible for moderate-resolution geostationary images, yielding values consistent with previous studies. This study offers new insights into exploring the transport mechanisms of green tides through remote sensing-driven velocity. Full article

The Western Pacific region is highly vulnerable to typhoon storm surge disasters, with localized erosion posing a particularly prominent issue for coastal marine structures. The prevalence of extreme typhoon storm surges poses a significant threat to the safety of engineering projects in these areas. In this study, a parameterized wind field model with precise calculation of wind speed was employed to establish a numerical model for typhoon storm tides. Based on the Western Pacific typhoon data from 1949 to 2023, hydraulic simulations were conducted for Hangzhou Bay, Xiangshan Port, and Yueqing Bay, revealing maximum flow velocities of 4.5 m/s, 1.95 m/s, and 2.09 m/s, respectively. These velocities exceeded the maximum possible tidal flow by 0.47–1.17 m/s. Additionally, using Sun’s velocity formula, the initiation flow velocities were calculated to be 1.85 m/s, 1.81 m/s, and 2.06 m/s for the aforementioned locations. Through localized erosion tests conducted around typical bridge piers and the subsequent application of similarity criteria, the maximum depth of localized erosion in the study area was determined to range from 2.16 m to 16.1 m, which corresponds to 1.1–2.3 times the scour caused by the maximum tidal flow scenario. A comparison of the erosion test results with calculations based on several formulas demonstrated that the scour prediction formula proposed by Sun exhibited the highest accuracy. This study supplements the understanding of the impact of typhoon storm surges on bridge pier erosion and provides a scientific basis for the design of bridge foundations. Full article

Assessing the hazard of inundation due to extreme storm surges in low-lying coastal areas and fragile ecosystems has become necessary and important. In this study, Xincun Lagoon and Li’an Lagoon in the Lingshui area of Hainan, China, were selected as the study areas, a high-resolution storm surge inundation numerical model was established, and the model reliability was tested. Based on data on typhoons affecting the study area from 1949 to 2022, the typhoon parameters for the extreme storm surge scenario were set and used for model numerical simulation and hazard assessment. The results revealed that in the extreme storm surge scenario, the average maximum tidal level, average maximum flow velocity, maximum inundation area, and average maximum inundation depth in the lagoon area were 2.29 m, 1.03 m/s, 14.8124 km², and 1.20 m, respectively. Under the extreme storm surge scenario, a flow velocity of 2.0 m/s off the coasts of the lagoons could damage coastal aquaculture facilities, harbors, and ecosystems, while an inundation depth exceeding 1 m along the coasts of the lagoons could lead to the salinization of inundated land and severely affect the safety of residents. The hazard analysis of storm surge inundation in the land area of the lagoons revealed that hydrographic nets and coastal wetlands are the major land types inundated by storm surges, with the two accounting for approximately 70% of the total inundation area. According to China’s technical guidelines, the hazard levels of the inundated land area of the lagoons are mostly level 3 (moderate hazard) and level 2 (high hazard), together accounting for approximately 90% of the total inundation area. If the government deems the measures feasible based on strict estimation and scientific evaluation of economic benefits and disaster prevention, planting mangroves in coastal wetlands and/or establishing adjustable tidal barriers at narrow entrances to lagoons could minimize disaster losses. Full article

This study presents an in-depth analysis of the dynamic beach landscapes of Hainan Island, which is located at the southernmost tip of China. Home to over a hundred natural and predominantly sandy beaches, Hainan Island confronts significant challenges posed by frequent marine natural disasters and human activities. Addressing the urgent need for long-term studies of beach dynamics, this research involved the use of CoastSat to extract and analyze shoreline data from 20 representative beaches and calculate the slopes of 119 sandy beaches around the island for the period from 2013 to 2023. The objective was to delineate the patterns of beach evolution that contribute to the prevention of sediment loss, the mitigation of coastal hazards, and the promotion of sustainable coastal zone management. By employing multi-source remote sensing imagery and the CoastSat tool, this investigation validated slope measurements across selected beaches, demonstrating consistency between the calculated and actual distances despite minor anomalies. The effective use of the finite element solution (FES) in the 2014 global tidal model for tidal corrections further aligned the coastlines with the mean shoreline, underscoring CoastSat’s utility in enabling precise coastal studies. The analysis revealed significant seasonal variations in shoreline positions, with approximately half of the monitored sites showing a seaward progression in summer and a retreat in winter, which were linked to variations in wave height. The southern beaches exhibited distinct seasonal variations, which contrasted with the general trend due to differing wave impacts. The western and southern shores showed erosion, while the northern and eastern shores displayed accretion. The calculated slopes across the island indicated that the southern beaches had steeper slopes, while the northern areas exhibited more pronounced slope variations due to wave and tidal impacts. These findings highlight the critical role of integrated coastal management and erosion control strategies in safeguarding Hainan Island’s beaches. By understanding the mechanisms driving seasonal and regional shoreline changes, effective measures can be developed to mitigate the impacts of erosion and enhance the resilience of coastal ecosystems amidst changing environmental conditions. This research provides a foundational basis for future efforts aimed at the sustainable development and utilization of coastal resources on Hainan Island. Full article

Locust plagues and tidal disasters are primary natural hazards in China’s eastern coastal regions, yet their interrelationship remains unclear. This study, drawing on historical documents from the Ming and Qing dynasties (1368–1911 AD), focuses on Zhejiang Province and its Hangzhou Bay coastline, areas typically affected by tidal disasters. Employing advanced quantitative analysis and spatiotemporal models, the research aims to reveal the mechanisms behind tidal disasters and their impact on locust population dynamics. The findings indicate a limited spatiotemporal correlation between locust plagues and tidal or drought disasters but a significant association with flooding. The relationship between locust infestations and floods is notably strong in the unique geographical context of Hangzhou Bay’s northern shore. The ‘hydromarginal’ nature of the north coast creates an ideal habitat for locusts. This study pioneers in identifying flooding as a crucial mediator between tidal disasters and locust plagues, shedding light on the ‘typhoon-tidal-flood-locust’ disaster sequence and offering new insights into understanding and mitigating natural disasters in the region. In this study, we primarily employ a quantitative methodology, utilizing advanced data analysis and sophisticated spatiotemporal modeling to investigate the interplay between locust plagues and tidal disasters. Although some progress has been made in the study of historical natural disasters, systematic studies of the relationship between tidal floods and locust breeding sites along the east coast of China during the Ming and Qing dynasties are still scarce. This study fills this gap by employing advanced GIS and time series analysis techniques, combining traditional historical documentary studies with modern scientific methods and providing a new methodological approach to the analysis of historical disaster patterns. Full article

Storm surge is one of the most remarkable natural calamities, which is shown as the abnormal sea level changes in the coastal waters during a typhoon event. To investigate the responses of storm surges to the typhoon paths, intensities and coastal dynamics, a coupled wave–current model is used to study the impacts of strong winds, considerable waves and complex currents on storm surges in the Yangtze River Estuary (YRE) during three representative typhoons of Fongwong (2014), Ampil (2018) and Lekima (2019) with different intensities and paths. The model is verified using the measured data on significant wave height and period, water level and current velocity and performs well in modeling real conditions. The numerical results demonstrate that (1) the maximum storm surge occurred in the South Channel (SC) during Fongwong and Lekima while in the North Branch (NB) during Ampil due to the typhoon path and the estuarine terrain. Among the three typhoons, Lekima presented the highest surge, with a maximum value of 1.17 m at SC2 (the inner point of the SC). There was a negative surge during Ampil, which reached −0.42 m at SC2, due to the representative path (SE to NW) and offshore wind action. (2) Tide is the main influencing factor of storm surge as the maximum or minimum value always occurs at the low or high tidal level, respectively. Meanwhile, typhoon intensity is important as it influences the variation rate of surge with higher intensity leading to a sudden increase in surge while the tidal intensity primarily affects the peak value. (3) The wave setup can counteract the wind-induced negative surge. The peak differences between storm surge isoline and wave setup isoline are 0.15, 0.2 and 0.2 m during Fongwong, Ampil and Lekima, respectively, which illustrates the impacts of the combined actions of the typhoon path and intensity on the wave setup. This research emphasizes the influences of wave–current interaction on estuarine storm surge during typhoon events and reveals the potential risks for oceanic disasters like coastal inundation. Full article

Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical and real-time data, BALSSA significantly enhances prediction accuracy. Utilizing a bidirectional attention-based LSTM framework, it captures complex, non-linear relationships and long-term dependencies, improving the accuracy of storm surge predictions. The enhanced model, D-BALSSA, further amplifies predictive capability through a doubled bidirectional attention-based structure. Training and evaluation involve a comprehensive dataset from over 70 typhoon incidents in Macao between 2017 and 2022. The results showcase the outstanding performance of BALSSA, delivering highly accurate storm surge forecasts with a lead time of up to 72 h. Notably, the model exhibits a low Mean Absolute Error (MAE) of 0.0287 m and Root Mean Squared Error (RMSE) of 0.0357 m, crucial indicators measuring the accuracy of storm surge predictions in water level anomalies. These metrics comprehensively evaluate the model’s accuracy within the specified timeframe, enabling timely evacuation and early warnings for effective disaster mitigation. An adaptive system, integrating real-time alerts, tropical cyclone (TC) chaser, and prospective visualizations of meteorological and tidal measurements, enhances BALSSA’s capabilities for improved storm surge prediction. Positioned as a comprehensive tool for risk management, BALSSA supports decision makers, civil protection agencies, and governments involved in disaster preparedness and response. By leveraging advanced machine learning techniques and extensive data, BALSSA enables precise and timely predictions, empowering coastal communities to proactively prepare and respond to extreme weather events. This enhanced accuracy strengthens the resilience of coastal communities and protects lives and infrastructure from the escalating threats of climate change. Full article

Amid mounting concerns about climate change’s impact on coastal areas, this study investigates storm surge dynamics induced by Tropical Depression 29W (TD 29W) using the MIKE 21 model. Comprehending the complex mechanisms behind storm surges is crucial considering gaps in understanding their combined influences, including tide–surge interactions, varying typhoon parameters, and changing storm tracks. The impacts of climate change, including accelerating sea level rise and its correlation with storm surge magnitudes, require detailed investigations for effective disaster management in vulnerable coastal communities. Through precise calibration, matching simulations with tidal gauge stations, this research uncovers the intricate interplay between landfall timing, diverse storm tracks, wind intensities, and the amplifying impact of rising sea levels. Findings indicate surge residuals ranging from −0.03m to 0.01m during TD 29W’s landfall, with higher surge residuals during rising tide phases. Moreover, an increase in TD 29W’s maximum wind speed moderately influences positive surges while significantly amplifying negative surge heights by 68% to 92% with wind speed increments. An analysis of typhoon track variations emphasizes the vulnerability of the Pahang coast to changing storm dynamics, underlining the need for tailored resilience strategies. Projections suggest a significant surge height increase by the year 2100, emphasizing the urgency of adaptive measures for the region. Full article

To comprehensively evaluate the seismic performance of offshore bridges, a seismic risk analysis of an example bridge was developed based on improved two-dimensional (2D) seismic fragility analysis. Taking a simply-supported beam bridge in an offshore tidal environment as an example, the adverse effects of chloride ion erosion are considered and the seismic response process of the example bridge is simulated using the Incremental Dynamic Analysis (IDA) method. The appropriate damage indexes are chosen for the plate rubber bearing and the pier, and the one-dimensional (1D) seismic fragility curves of single components and the entire bridge are obtained. The correlation coefficients of vulnerable components are quantitatively proposed based on the correlation analysis method, and the 2D seismic fragility curves of the entire bridge are achieved while accounting for seismic correlation between vulnerable components. The seismic risk probability of the entire bridge is finally determined after combining hazard analysis at the bridge site and seismic loss analysis. The results show that there is a significant correlation between vulnerable components and that 2D seismic fragility analysis based on the reliable correlation coefficients of vulnerable components can more comprehensively evaluate the seismic performance of the entire bridge. In the context of seismic disaster reduction research focused on slight and moderate damage caused by moderate-to-small ground motions, this research can provide scientific and technical support for seismic design and seismic risk assessment of offshore bridges. Full article

Storm surges are one of the most severe marine hazards, causing fatalities and devastating infrastructure. It is important to conduct research on storm surge hazards to achieve disaster avoidance and the protection of local populations. In this study, the Regional Ocean Modeling System (ROMS) was used to develop a framework to simulate the inundation (using the wet/dry method) of land in Ningbo, China during an extreme typhoon storm surge. The baseline simulation with the realistic typhoon intensity and track was well validated by meteorological and ocean tidal observations. Using reanalysis and an asymmetric typhoon wind field from the Holland model as atmospheric forcing, we presented different storm surge inundation scenarios regarding various intensities and tracks. The results revealed that typhoon storm surges are significantly affected by both the intensities and tracks of typhoons. Specifically, when Ningbo was located in the navigable semicircle, increasing the typhoon intensity not only resulted in the total inundation area of the whole study area from 108.57 km² to 139.97 km², but also led to significant negative storm surges in some sea areas. When Ningbo was exposed to the dangerous semicircle of the intensified typhoon, the storm surge along the coast of the Xiangshan Bay could exceed 4 m, amplifying the total inundation area to 245.41 km². Thus, it was evident that the location of the impacted region within the typhoon’s wind field plays a critical role in determining the severity of the storm surge. These results provide valuable suggestions for storm surge disaster prevention and mitigation for local governments. Full article

Both human activities and climate change have changed landscapes significantly, especially in coastal areas. Sea level rise and land subsidence foster tidal floods and permanent inundations, thus changing and limiting land use. Though many countries, including Indonesia, are aware of these phenomena, the legal status of this permanently inundated land remains unclear. Indonesia refers to this land legally as obliterated land. This qualification makes former landowners uncertain, as it does not recognize their previous land rights, and creates disputes during land acquisition. In view of policy pressures to develop large-scale projects, the government often fails to include obliterated land legally during land acquisition processes for these projects. This causes unfair and disputed compensation for those former landowners. Current scientific discourses do not yet address this legal quandary. This study therefore has the following three aims: (1) to describe the legal, institutional and procedural contradictions related to obliterated land; (2) to assess the validity of right of the owners whose land parcels are permanently inundated; and (3) to formulate a responsible and tenure responsive policy to deal with obliterated land. We investigate these questions for the construction of a toll road and sea embankment in Kecamatan Sayung, Kabupaten Demak involving obliterated land. We reviewed policies, regulations and documentations related to coastal land and disaster management, and the implementation of land acquisition. We used geospatial data to visualize the ways in which and locations where landscapes, land parcels and land right changed. We determined that legal uncertainty leads to policy inconsistencies in handling obliterated land, specifically during land acquisition. Additionally, former landowners suffer from the legal gaps to establish clarity of land tenure, which prevents them from receiving any compensation. We suggest a law revision that considers the social–historical aspects of land tenure when defining obliterated land. The law should also provide for a fairer and more just compensation for former landowners during land acquisition processes. Full article

The estuary delta is an area where human economic activities are active and natural ecological environment is fragile. With global change and the intensification of human activities, coastal and seabed erosion around the world is becoming more and more serious. In this paper, we used the Delft 3D numerical simulation to compare the hydrodynamic effects of sediment transport paths in the Yellow River delta (river-controlled type), Yangtze River delta (tidal type) and Mekong River delta (tidal wave type) in the East Asian monsoon area, and analyzed the causes of accumulation erosion landform distribution in three different types of subaqueous deltas. This study finds the Yellow River Delta has experienced varying degrees of erosion at the estuary, but its subaqueous delta is still dominated by deposition; the Yangtze River Delta has ensured the stability of its shoreline under the influence of artificial shoreline reinforcement, but the subaqueous delta (water depth: 0–15 m) is in a state of erosion all year round; and in the Mekong River Delta the erosion occurs in both its shoreline and subaqueous delta. Additionally, only by analyzing the erosion and deposition within the transport range of resuspended sediment, the changes in the properties of the entire subaqueous delta could be recognized. The research results can not only be helpful to analyze whether the change of river sediment will lead to the change of delta type under human influence, but also provide more powerful scientific support for the protection of delta ecological environment, geological environment safety and geological disaster prevention. Full article