Projections of coastal flooding under different RCP scenarios over the 21st century: A case study of China's coastal zone

Abstract

Among sea level rise (SLR)-induced threats to coastal regions, flooding inundation is considered the most serious, especially in China's coastal zone (CCZ). Previous attempts to simulate the inundation area caused by SLR at the macro spatial scale usually employed the bathtub model based on a large number of historical tide station data. However, these efforts mainly focused on simplifying the calculation of the inundation process at a large scale, but failed to improve the accuracy mainly due to the failure to exclude isolated depressions. Here, based on regional sea level data of the Integrated Climate Data Center (ICDC), the University of Hamburg, a physical ocean model (Finite Volume Coast and Ocean Model, FVCOM) was employed to simulate the maximum possible regional SLR in offshore China from 2021 to 2100 under the representative concentration pathways (RCPs) 2.6, 4.5, and 8.5 scenarios, respectively, and the hydraulic connectivity model was applied based on multi-source data to improve the simulation accuracy of the inundation area. Through these improved methods, the maximum possible inundation characters of CCZ over the 21st century were observed as follows: (1) Under RCP2.6/4.5/8.5 scenarios, the macro spatial patterns of inundation areas are similar, with the inundation area mainly distributing in the coastal areas around the Bo Hai, the coastal areas from Jiangsu to the estuary of the Yangtze River, the Pearl River Delta, and the western coastal areas of Taiwan, and the inundation area gradually expands and spreads over time. (2) Under the three scenarios, the maximum possible inundation area shows a wavelike rising trend, and by 2100, the inundation area is expected to exceed 48.89 × 103 km2 (RCP 2.6) and reach a maximum of 53.50 × 103 km2 (RCP 8.5). (3) There are significant differences in the inundation depth and flood frequency under the three scenarios, with the inundation characters of the high-emission scenario being the most extreme. Overall, our work can provide meaningful information for proper coastal urban planning, disaster prevention, and mitigation measures selection in China. The methodology can also be used for predicting the inundation areas in other coastal countries facing the threat of SLR.