Hangzhou Bay is a large, high-turbidity shallow bay located on the southern side of the Changjiang Estuary, China. The process and dynamic mechanisms of water and sediment transport in the bay are not yet clear. An improved three-dimensional sediment numerical model that combined various dynamic factors was established to simulate and analyze these mechanisms. The residual current cannot properly represent the net water and sediment transport, and the residual unit width water flux (RUWF) and residual unit width sediment flux (RUSF) were used to explain the water and sediment transport. The results of numerical simulations indicate that in summer, the surface RUWF from the Changjiang Estuary near Nanhui Cape flows westward along the coast, in which the major part flows southward to the Zhenhai area, and the small part flows further westward along the north coast and then turns to the south coast and eastward, forming the water transport pattern of north-landward and south-seaward, which is stronger in the spring tide than in the neap tide. The bottom RUWF near Zhenhai flows northward to Nanhui Cape in the neap tide, which is larger in the neap tide than in the spring tide. In the middle and western parts of the bay, the RUWF has the same pattern as the surface water transport and is stronger in the spring tide than in the neap tide. The pattern of RUSF is roughly similar to the water flux transport. During the spring tide, the water and sediment transport fluxes near Nanhui Cape are from the Changjiang Estuary into Hangzhou Bay, but from Hangzhou Bay into the Changjiang Estuary during the neap tide. In the winter, the distributions of RUWF, RUSF, and suspended sediment concentration (SSC) are similar to those in the summer. In addition, the distance of surface water transport westward along the north coast is shorter than that in the summer, the magnitude of the bottom RUWF is smaller than that in the summer due to the weaker salinity gradient, and the bottom RUSF near Nanhui Cape is weaker than that in the summer during the neap tide. The net transect water flux (NTWF) and the net transect sediment flux (NTSF) near Nanhui Cape are from the Changjiang Estuary into Hangzhou Bay during the spring tide; during the neap tide, the NTWF is still from the Changjiang Estuary into Hangzhou Bay, but the NTSF is from Hangzhou Bay into the Changjiang Estuary because the SSC is much higher in the bottom layer than in the surface layer. The dynamic reason for the temporal and spatial variation in RUWF and RUSF is that the barotropic pressure gradient force is larger than the baroclinic pressure gradient force during the spring tide and is the opposite during the neap tide.

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.

Morphodynamic variabilities of tidal flats (e.g., the variations of pattern, area, and topography) are a key issue in the management of coastal intertidal zones. In this study, the morphodynamic variabilities of the Lin-gang (Shanghai) tidal flat were investigated using waterlines extracted from multi-source satellite images acquired from 2013 to 2020. The waterlines were evaluated against in situ measurements. The results of our investigation indicated that the tidal flat was in a state of rapid accretion from 2015 to 2018, and in a state of erosion from 2018 to 2020. We found that the accretion of the tidal flat was most likely due to the protection of local vegetation, which prevents the sea bottom from eroding. However, storms have primarily been causing erosion since 2018. The potential mechanisms of the geomorphological variations were further analyzed using the empirical orthogonal function (EOF) method. The analysis revealed that the variation in the tidal flat was dominated by two modes. The first mode accounted for 55% of the variation, while the second mode accounted for 18%. The spatial distribution of the first mode was highly related to the artificial vegetation, indicating that the local variations in the vegetation prevented the sea bottom from eroding, which was dominant in the accretional phase from 2015 to 2018. The second model reflected the extreme meteorological events that resulted in potential changes in the tidal flat’s pattern (i.e., transitioning to an erosion phase from 2018 to 2020). The satellite-derived topographies were demonstrated to be an effective means of mapping the evolution of a meso-tidal flat.

This paper addresses the intricate challenges of coastal management, particularly in rapidly forming tidal flats, emphasizing the need for innovative monitoring strategies. The dynamic coastal topography, exemplified by a newly formed tidal flat in Shanghai, underscores the urgency of advancements in coastal risk recognition. By utilizing a digital twin framework integrated with state-of-the-art unmanned aerial vehicles (UAVs), we systematically evaluate three configurations and identify the optimal setup incorporating real-time kinematics (RTK) and light detection and ranging (LiDAR). This UAV configuration excels in efficiently mapping the 3D coastal terrain. It has an error of less than 0.1 m when mapping mudflats at an altitude of 100 m. The integration of UAV data with a precise numerical ocean model forms the foundation of our dynamic risk assessment framework. The results showcase the transformative potential of the digital twin framework, providing unparalleled accuracy and efficiency in coastal risk recognition. Visualization through Unity Engine or Unreal Engine enhances accessibility, fostering community engagement and awareness. By predicting and simulating potential risks in real-time, this study offers a forward-thinking strategy for mitigating coastal dangers. This research not only contributes a comprehensive strategy for coastal risk management but also sets a precedent for the integration of cutting-edge technologies in safeguarding coastal ecosystems. The findings are significant in paving the way for a more resilient and sustainable approach to coastal management, addressing the evolving environmental pressures on our coastlines.

Estuarine reservoirs are critical for freshwater supply and security, especially for regions facing water scarcity challenges due to climate change and population growth. Conventional methods for assessing drought severity or monitoring reservoir water level and storage are often limited by data availability, accessibility and quality. We present an approach for monitoring estuarine reservoir water levels, storage and extreme drought via satellite remote sensing and waterline detection. Based on the CoastSat algorithm, Landsat-8 and Sentinel-2 images from 2013 to 2022 were adopted to extract the waterline of Qingcaosha Reservoir, the largest estuarine reservoir in the world and a key source of freshwater for Shanghai, China. This study confirmed the accuracy of the satellite-extracted results through two main methods: (1) calculating the angle of the central shoal slope in the reservoir using the extracted waterline data and measured water levels and (2) inverting the time series of water levels for comparison with measured data. The correlation coefficient of the estimated water level reached ~0.86, and the root mean square error (RMSE) of the estimated shoal slope was ~0.2°, indicating that the approach had high accuracy and reliability. We analyzed the temporal and spatial patterns of waterline changes and identified two dates (21 February 2014 and 15 October 2022) when the reservoir reached the lowest water levels, coinciding with periods of severe saltwater intrusions in the estuary. The extreme drought occurrences in the Qingcaosha Reservoir were firstly documented through the utilization of remote sensing data. The results also indicate a strong resilience of the Qingcaosha Reservoir and demonstrate that the feasibility and utility of using satellite remote sensing and waterline detection for estuarine reservoir storage can provide timely and accurate information for water resource assessment, management and planning.

With the development of unmanned aerial vehicle (UAV) technology, its application in scientific fields, such as coastal monitoring, has become increasingly prevalent. The utilization of multi-sensor UAV platforms has made unmanned and highly repetitive monitoring feasible. This study employs three commercially available UAV systems with representative characteristics to generate Digital Surface Models (DSM) in the Lingang Tidal Flat (Shanghai), and evaluate the DSMs produced by each UAV comparing with the in-situ measurements. According to the results, the beach DSM produced by the non-RTK UAV using the Structure From Motion (SFM) algorithm is likely to be unusable. In contrast, the RTK-equipped UAV achieved an elevation accuracy of 0.3–0.54 m. The addition of a laser radar (Lidar) module further improved the accuracy to 0.1 m, significantly enhancing its reliability for comparison. We also explored a method for reconstructing the beach DEM by detecting waterlines from UAV imagery. Furthermore, during the discussion, the accurate DSM was utilized to explore the potential application of UAV technology in coastal management visualization. By adopting the digital twin framework, it would be possible and efficient to create a realistic representation of the coast, integrating actual ocean data such as tidal levels. This approach could enable the identification of coastal risks with relative ease.