Integrating relative sea level rise into compound flooding hazard assessment for coastal cities

Haikou, the capital city of Hainan Province, China. We develop a framework combining statistical and hydrodynamic overland inundation modeling to assess the combined effect of compound flooding and relative sea level rise (RSLR) on urban functional areas in terms of flood hazard and exposure. This framework uses maximum flood depth (MFD), maximum flood area, and maximum flood volume (MFV) as metrics for flood hazard assessment, and absolute flood exposure and relative flood exposure (RFE) metrics for evaluating flood exposure in urban areas. Our findings indicate that flood hazards assessed using univariate return periods (RPs) are more severe than those derived from bivariate RPs, with the univariate MFV being 90.5 % higher for the 200-year RP. RSLR significantly amplifies the compound flood effects in major inundation areas, such as Haidian Island and Jiangdong New Area. Specifically, for the 100-year RP, the MFD in these regions is projected to increase by 1.5 m by 2100 due to RSLR. Additionally, residential land is identified as the most vulnerable to RSLR, with its RFE reaching 19.8 % by 2100, which is 6.3 % higher compared to scenarios without considering RSLR. These results show the necessity of incorporating RSLR into compound flood hazard assessment to formulate flood mitigation and management measures in coastal cities. • Propose a framework integrating statistical-hydrodynamic models to assess compound flood hazard and exposure under RSLR. • Bivariate analysis more accurately captures flood risks from storm tide and rainfall than univariate approaches. • RSLR significantly increases MFD and MFV by 2100, especially in critical urban areas. • LS amplifies flood hazards, affecting over 50 % of flood-exposed areas by 2100. • Residential land is highly vulnerable to rising flood risks under RSLR, necessitating targeted mitigation strategies.