Evaluation of sea level rise and associated responses in Hangzhou Bay from 1978 to 2017

An extreme sea level event along the northwest coast of the South China sea in 2011–2012

As global warming problem is becoming serious in recent decades, the global sea level is continuously rising. This will cause damages to the coastal deltas with the characteristics of low-lying land, dense population, and developed economy. Continuously reclamation costal intertidal and wetland areas are making Shanghai, the mega city of Yangtze River Delta, more vulnerable to sea level rise. In this paper, we investigate the land subsidence temporal evolution of patterns and processes on a stretch of muddy coast located between the Yangtze River Estuary and Hangzou Bay with differential synthetic aperture radar interferometry (DInSAR) analyses. By exploiting a set of 31 SAR images acquired by the ENVISAT/ASAR from February 2007 to May 2010 and a set of 48 SAR images acquired by the COSMO-SkyMed (CSK) sensors from December 2013 to March 2016, coherent point targets as long as land subsidence velocity maps and time series are identified by using the Small Baseline Subset (SBAS) algorithm. With the DInSAR constrained land subsidence model, we predict the land subsidence trend and the expected cumulative subsidence in 2020, 2025 and 2030. Meanwhile, we used altimetrydata and densely distributed in the coastal region are identified (EEMD) algorithm to obtain the average sea level rise rate in the East China Sea. With the land subsidence predictions, sea level rise predictions, and high-precision digital elevation model (DEM), we analyze the combined risk of land subsidence and sea level rise on the coastal areas of Shanghai. The potential inundated areas are mapped under different scenarios.

Potential inundated coastal area estimation in Shanghai with multi-platform SAR and altimetry data

Because of global warming, the sea level is expected to continue to rise, possibly having a significant impact on the intensities and spatial distribution characteristics of coastal storm surges. In this study, we took super typhoon Rammasun (2014) as a case study and applied the SCHISM (Semi-implicit Cross-scale Hydroscience Integrated System Model) to simulate storm surge in the northwestern South China Sea under future sea level rise (SLR) scenarios. To improve the accuracy of storm surge hindcast, we used reconstructed wind field to drive the model in which ERA5 reanalysis data were superposed on the wind field calculated from the Holland parametric cyclone model. The results show that the storm surge hindcast was significantly improved by using this reconstructed wind forcing. 2-D and 3-D model hindcast capabilities were compared; the 3-D model reproduced the storm surge better. The regional sea level projections in 2050, 2100, 2200, and 2300 for RCP 4.5 scenarios (provided by the IPCC AR6 dataset) were superposed on the original water depth as the predicted sea levels, then those depths were used in models of storm surge in the study area under a typhoon identical to Rammasun. Model results demonstrate that storm surge peaks in most sea areas decrease nearly linearly with SLR, especially in regions of high surges.

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Model Simulation of Storm Surge in the Northwestern South China Sea Under the Impact of Sea Level Rise: A Case Study of Super Typhoon Rammasun (2014)

15 This study aims to analyze statistical behavior of the continental shelf wave 16 motions, including continental shelf waves (CSWs) and arrested topographic waves 17 (ATWs), in the northern South China Sea. The baseline consists of tide-gauge data from 18 stations Kanmen, Xiamen, Shanwei, Hongkong, and Zhapo, as well as along-track sea 19 level anomaly (SLA) data derived from multiple satellite altimeters from 1993 to 2020. 20 The subtidal signals propagating along the coast with periods shorter than 40 d and 21 phase speeds of about 10 m s -1 are interpreted as CSWs. The cross-shelf structure of 22 along-track SLA indicates that mode 1 of CSWs is the predominant component trapped 23 in the area shallower than about 200 m. The amplitudes of CSWs reach the maximum 24 0.6 m during July-September, and minimum 0.2 m during April-June. The inter- 25 seasonal and seasonal signals represent ATWs. The amplitudes of ATWs reach 0.10 m 26 during October-December, twice of that during July-September. These observations are 27 well interpreted by the framework of linear wave theory. The cross-shelf structures of 28 CSWs and ATWs derived from along-track SLA illustrate that the methods are suitable

Statistical analysis of dynamic behavior of continental shelf wave 1 motions in the northern South China Sea

On the basis of the satellite maps of sea level anomaly (MSLA) data and in situ tidal gauge sea level data, correlation analysis and empirical mode decomposition (EMD) are employed to investigate the applicability of MSLA data, sea level correlation, long-term sea level variability (SLV) trend, sea level rise (SLR) rate and its geographic distribution in the South China Sea (SCS). The findings show that for Dongfang Station, Haikou Station, Shanwei Station and Zhapo Station, the minimum correlation coefficient between the closest MSLA grid point and tidal station is 0.61. This suggests that the satellite altimeter MSLA data are effective to observe the coastal SLV in the SCS. On the monthly scale, coastal SLV in the western and northern part of SCS are highly associated with coastal currents. On the seasonal scale, SLV of the coastal area in the western part of the SCS is still strongly influenced by the coastal current system in summer and winter. The Pacific change can affect the SCS mainly in winter rather than summer and the affected area mostly concentrated in the northeastern and eastern parts of the SCS. Overall, the average SLR in the SCS is 90.8 mm with a rising rate of (5.0±0.4) mm/a during 1993–2010. The SLR rate from the southern Luzon Strait through the Huangyan Seamount area to the Xisha Islands area is higher than that of other areas of the SCS.

Qingtao Song

Papers

A study of sea level variability and its long-term trend in the South China Sea