Coastal vegetation can reduce extreme water levels during storm events, but the controlling factors and processes in complex estuary or delta systems are still unclear. This limits an effective implementation of nature-based coastal defences in delta mega-cities in low-lying coastal areas. Here we have numerically modelled how mangroves can offer coastal protection to the large coastal cities located in the Pearl River Delta (China), such as Guangzhou and Shenzhen, during strong typhoons, like Hato (2017). Water level attenuation by mangroves is effective during extreme water level conditions and differences in mangrove forests’ properties drive their coastal protection function. The local (within-wetland) attenuation of extreme water levels is more effective with wide vegetation patches and higher vegetation drag. Narrower vegetation patches can still provide non-local (upstream) water level attenuation if located in the upper estuary channels, but their design needs to avoid amplification of water levels in other delta areas.

The Pearl River Delta contains the world's largest urban area in both size and population. It is a low‐lying flood‐prone coastal environment exposed to sea level rise (SLR) and extreme water levels caused by typhoons. A Finite Volume Community Ocean Model implementation for the South China Sea and the Pearl River Delta is used to understand how future SLR, tides, and typhoon storm surges will interact and affect coastal inundation. The SLR signal and extreme surge levels provide the major contributions to flooding; however, amplification of tides could exceed 0.5 m for 2.1 m SLR and should be considered when planning future coastal defences. On the other hand, if typhoons like Hato or Mangkhut, the latest and strongest ones hitting the area, were to happen in the future, a surge level reduction up to 0.5 m could be expected in coastal areas.

Acknowledgments This work was partly supported by joint Engineering and Physical Science Research Council (EPSRC) UK and Technology Foundation STW Netherlands funded SINBAD (EP/J005541/1) project. P. Zheng was supported by the China Scholarship Council during his four-year PhD study at the University of Liverpool. We would like to thank Prof. C.S. Chen of the University of Massachusetts-Dartmouth for providing the source code of FVCOM and also the SWAN developers for developing and providing this open source code. We would also like to thank the staff and personnel involved in collecting and maintaining the DUCK’94 experiment dataset and the anonymous reviewers for their constructive comments and suggestions. Computational support was provided by the Chadwick High Performance Computer at University of Liverpool and also the facilities of N8 HPC Centre of Excellence, provided and funded by the N8 consortium and EPSRC (EP/K000225/1).Peer reviewedPublisher PD

Short‐term bed level changes play a critical role in long‐term coastal wetland dynamics. High‐frequency observation techniques are crucial for better understanding of intertidal biogeomorphic evolution. Here, we introduce an innovative instrument for bed level dynamics observation, that is, LSED‐sensor (Laser based Surface Elevation Dynamics sensor). The LSED‐sensors inherit the merits of the previously introduced optical SED sensors as it enables continuous high‐frequency monitoring with relatively low cost of labor and acquisition. As an iteration of the optical SED‐sensors, the LSED‐sensors avoid touching the measuring object (i.e., bed surface), and they do not rely on daylight by adapting laser‐ranging technique. Furthermore, the new LSED‐sensors are equipped with a real‐time data transmission function, enabling automatic observation networks covering multiple (remote) sites. During a 22‐day field survey in a mangrove wetland, good agreement (R² = 0.7) has been obtained between the automatic LSED‐sensor measurement and an accurate ground‐truth measurement method, that us, Sedimentation Erosion Bars. The obtained LSED‐sensor data were subsequently used to develop machine learning predictors, which revealed the effect of vegetation is a main driver in the accumulative and daily bed level changes. We expect that the LSED‐sensors can further support machine learning applications to extract new knowledge on coastal biogeomorphic processes.