## 3 Results
### Future Flood Risk Evolution Driven by Multiple Assessment Factors  
#### 3.2.1 Future Scenario-Based Flood Risk  
Figure 8 shows the trends of the assessment factors driving the evolution of the baseline scenario. In the next 30 years, the PLEEZ may experience a trend of more severe extreme rainfall (Figure 8a). In the RCP4.5 pathway scenario, RMAX3 would remain stable in the 2030s (with an even decreasing trend in the south; Figure S7), increase significantly in the 2040s and then decrease, while in the RCP8.5 pathways scenario, it would reach a maximum value in the 2030s and decrease thereafter. The population density in the urban centers (represented by outliers at the top of the boxplots in Figure 8b) would have a high growth rate, while that in non-urban areas (i.e., most of the PLEEZ; Figure S8) would decrease markedly, constituting a polarized pattern of population development. GDP would continue to grow in all scenarios (Figure 8c and Figure S9), with the SSP5 pathway presenting a more incremental trend. The FLUS model-generated land use in 2020 is in good agreement with the actual land use in 2020, with a Kappa coefficient of 0.96 and an overall accuracy of 0.97. The projection based on the change from 2015 to 2020 indicates that water and wetland will increase, while croplands, grassland and forests will decrease conversely (Figures 8d and S10 and Table S5). The estimated future factors were normalized and input into the baseline model to map the scenario-based flood risk maps (Figure 9), where the thresholds for delineating each risk level were consistent with the baseline map to facilitate the observation of changes. For  
Figure 8: Future trends of the assessment factors in the baseline model. (**a–c**) show the change ratios of RMAX3, population density and GDP for the whole PLEEZ in different periods (compared with the 2020s), respectively. (**d**) Area percentages of land-use types in future periods. a given grid in the PLEEZ, the intensity of flood risk change was measured by subtracting the risk level in the baseline map from that in the projected map (Figure 10). First, the overall spatial distribution pattern of risk with high levels in central areas and low levels in peripherical areas remained basically unchanged. Combined with the area changes of different risk levels (Figure 11a), there are several evolutionary trends that warrant vigilance. In the SSP2-RCP4.5 scenario, risk would decrease in the 2030s, peak in the 2040s (reflected by a 19.29% increase in the areas at high risk), and then decrease remarkably in the 2050s (reflected by a 12.4% decrease in the areas at very high risk). While  
Figure 10: The intensity of flood risk change in the PLEEZ in different future scenarios. Significant increase (2) in the legend represents a 2-step increase in the risk level compared with the baseline map, e.g., from low to high. Similarly, (\(-2\)) represents a 2-step decrease in risk level, and (1)/(\(-1\)) represent a 1-step increase/decrease, respectively, while (0) means that the risk level remains unchanged. The zoomed-in view presents the intensity of risk change along the Gan River in a 10 km buffer zone that covers the provincial capital and other non-urban areas along the river. Figure 9: Spatial distributions of future flood risk in the PLEEZ in different scenarios. The zoomed-in view presents the risk along the Gan River in a 10 km buffer zone that covers the provincial capital and other non-urban areas along the river. in the SSP5-RCP8.5 scenario, the risk peak would arrive earlier and be more severe in the 2030s (reflected by the increases of 12.29% and 38.96% in the very high and high-risk areas, respectively, for a total area of 2072 km\({}^{2}\)). Despite the reduced expected risk in the 2040s and 2050s, there would still be a significant risk increase compared with the baseline scenario.