## 3 Results and Analysis  
### Quantitative Impact of Climate Change on Compound Flood Drivers  
#### 3.1.1 Sea Level Rise  
Climate change is causing the global average sea level to rise by 3-4 mm annually due to ocean warming and glacier melting, and this rate is accelerating [34; 35]. According to the sixth assessment report (AR6) released by the Intergovernmental Panel on Climate Change (IPCC), it is projected that by 2100, the global average sea level will rise by 0.3-1.6 m under the RCP 8.5 high emission scenario; even if greenhouse gas emissions are significantly reduced, the sea level is still expected to be 0.3-0.6 m higher than today [36]. In regions with minimal sea level changes, even a rise of 0.1 m can result in a doubling of flood frequency [37]. In studying the impact of global sea level rise (SLR) on compound floods, many scholars have used the SLR data provided by the IPCC to predict future sea levels. Based on IPCC AR5, [PERSON] et al. (2018) [38] set the SLR for 2030 and 2050 to 0.13 m and 0.25 m, respectively, based on a projected 2100 SLR of 0.53-0.97 m. [PERSON] et al. (2023) [39] determined the sea levels for 2050 and 2090 under the RCP 4.5 and RCP 8.5 scenarios. [PERSON] et al. (2024) [40] conducted a study using the latest sea level rise prediction data from different climate scenarios of the IPCC AR6. On the other hand, some scholars have employed alternative methods to obtain SLR data. For instance, [PERSON] and [PERSON] (2016) [41] aggregated spatial projections of individual sea level components in a probabilistic manner. Their future sea level prediction results for the RCP 8.5 future climate scenario indicate the median (50 th percentile) and upper limit (95 th percentile) by 2050 (0.3 m and 0.5 m, respectively) and by 2100 (0.9 m and 2.1 m, respectively) of the regional sea level projections. There are two main computational methods for assessing the impact of SLR on compound floods in coastal areas. The first method is statistical analysis, such as using copulas and bivariate dependence analysis. For example, [PERSON] et al. (2017) [42] quantified the increases in flood probabilities caused by SLR based on 2030 and 2050 projections under representative concentration paths (RCPs) 4.5 and 8.5. The increase in the failureprobability of flood defenses along the coasts of the United States was shown to be strongly affected by SLR. The second method is numerical simulation. [PERSON] et al. (2021) [PERSON] et al. (2021) [43] applied a high-resolution hydraulic model to simulate peak flood depth, peak velocity, and flood duration under four climate change scenarios. Research has shown that in the 2080 scenario with high emissions of RCP 8.5 and a sea level rise of 0.56 m, the flood extent increased from 17.6% in the current baseline scenario to 19.4%, and the maximum flood duration increased from 3 h to 7 h, an increase of over 100%. This is because the rise in sea level slowed down the drainage of urban drainage systems into rivers. Table 1 presents numerical models used for simulating the impact of SLR on compound floods, including Delft3D, MIKE 21, FVCOM, and HEC-RAS. On the one hand, SLR directly affects the level and frequency of floods. On the other hand, it alters water depth and modifies the propagation of waves and storm surges, leading to indirect hydrodynamic effects [37]. As sea levels rise, the severity of storm surges increases [48]. The combination of SLR and storm surges represents the most significant flood risk in coastal areas [49]. This heightened risk is due to elevated coastal high tide levels, a shortened return period for extreme water levels, enhanced wave currents, and the insufficient disaster prevention capabilities of port engineering, flood control, and drainage facilities. These factors can result in storm surges reaching heights of 1.8 to 2.4 m in low-lying communities [50]. Comparative analyses of events like Hurricane Sandy have shown that SLR alone significantly increased the compound flooding hazard more than changes in storm climatology [40].