## 4. Discussion
### Numerical Simulations  
The greatest advantage of using numerical simulation methods to study the impact of climate change on compound floods is their ability to visualize the flood evolution process and outcomes [100]. The basic framework is to input driving data reflecting climate change (such as rainstorm feature change, sea level rise, etc.) and urbanization (such as changes in underlying surface and pipe network) into flood models, which can more accurately assess the disaster mechanism and impact law of climate change and urbanization on urban flood process. Currently, these methods are widely employed. Compound flooding under climate change results from the combined effects of multiple factors, including sea level rise, storm surges, extreme precipitation, and urbanization. Therefore, numerical simulation methods can be categorized into three dominant modes: ocean factor dominant, extreme precipitation dominant, and hydrodynamic dominant under climate change. Urbanization factors are integrated into the simulation process of each mode and have been shown to play a significant role, as illustrated in Figure 4.  
Compound flood simulations dominated by ocean factors under climate change takes precipitation and runoff simulation or observational data as boundary condition inputs, focusing on flood inundation under the combined effects of sea level rise, storm surges, waves, and other factors caused by climate change. Commonly used models include Delft3D, ADCIRC, FVCOM, and POM [33]. However, a drawback of these models is their limited consideration of precipitation and river floods caused by tropical cyclones less, potentially leading to an underestimation of compound inundation results [32]. [PERSON] et al. (2022) [12] used ADCIRC to simulate storm surges and found that by 2100, the frequency of joint extreme events in the northeastern United States will increase by 30-135 times. On 96% of coastlines, the overall impact of storm climate change on extreme compound events will surpass that of SLR. Similarly, using the ADCIRC model, national hydrological reports, and tidal station data, [PERSON] et al. (2022) [104] found that the primary driver of the coastal river compound flooding in the Pearl River Delta, China over the past 60 years was the extreme sea levels. Under the scenario of 2055 (RCP8.5), when the extreme typhoon and SLR occur simultaneously, the extreme water level in the region will increase by 0.76 m on average. If the upstream annual average flood is further superimposed, the average rise of the river network will reach 2.33 m. [PERSON] et al. (2023) [105] used the Delft3D model to conclude that tropical cyclones have a greater impact on future coastal compound flooding in Shanghai, China, than SLR. Figure 4: Three main modes for numerical simulation of compound floods under climate change. Compound flood simulations dominated by ocean factors primarily use 2D and 3D models. While 3D models provide more detailed predictions than 2D models, they require significantly more computational resources and large computing platforms. FVCOM and ADCIRC, which use unstructured grids, offer greater flexibility and accuracy in handling complex coastal terrains but come with higher computational costs. In contrast, Delft3D and POM perform well in more regular regions but may be less effective in managing complex boundary conditions. The compound flood model dominated by extreme rainfall under climate change uses observational data or simulated sea level as boundary condition inputs, primarily to simulate the entire process of extreme rainfall runoff inundation caused by climate change. Common models include SWMM, MIKE SHE, and VIC. However, due to the neglect of the interaction between storm surges and surface runoff, the inundation results may be underestimated in the extreme precipitation dominant models. [PERSON] et al. (2023) [39] applied the SWMM model to study the impact of SLR and future precipitation changes on flood depth in Haikou City, China, finding increases of 10.6% and 32.1%, respectively.