4. DISCUSSION
4.1 Influencing factors
Urban hydrological processes have altered with urban development, and
thus distribution pattern of flooding points has changed (Chang et al.,
2021; Zhao et al., 2014). In this study, we integrated a series of
driving factors (i.e., topography, building metrics) and identified the
major driving factors of urban flooding by adopting BRT method. The
reasons for urban flooding are complex, including natural and
anthropogenic factors. Through BRT analysis, land cover configuration
has the greatest impact on flooding. The fragmentation of green spaces
would increase occurrence of urban flooding events on a given land cover
composition (Zhang et al., 2020). Higher fragmentation at landscape
level might lead to serious urban flooding, and its role varied across
different megacities in China (Li et al., 2022). In our study, it
suggests that the optimal landscape shape index was nearly 2.8. The
results were not consistent with previous studies, the difference was
caused by the response variable selected, explanatory factors selected,
analysis units, and analysis methods. Similarly, contribution of patch
density was only 2.54%, which reflects the patch density was not
important.
Through BRT analysis, land cover composition has important impact on
flooding. Increasing the green space and waterbody could mitigate the
flooding risks. It was also suggested that increase the impervious
surfaces would be inevitably increase urban flooding risks. This is
attributed to large difference between impervious surfaces and
green-blue space (green space and waterbody). Specifically, it is
difficult for waterflow to penetrate into the impervious surfaces.
Additionally, less surface roughness of the impervious surface promotes
rainwater flow and accumulation. On the contrary, urban green space can
reduce runoff and regulate rainwater storage. This finding is consistent
with case study in other cities (Zhang et al., 2020). Zhang et al.
(2020) found that land composition plays a dominant role in determining
urban floods with analysis scale range from 1 km to 5 km in metropolitan
coastal cities of Guangzhou, China (Zhang et al., 2020).
Fewer studies explored the impact of the building metrics on urban
flooding, and the importance of building has not yet been agreed. For
example, Lin et al. (2022) found that 3D building metrics exert the
greatest influence on urban flooding. Li et al. (2022) revealed 3D
building metrics had slight impact on urban flooding. Our results
suggested that 3D building morphology played an important role in urban
flooding, and the contribution of 3D building morphology was equal to
that of land cover composition. In fact, contributions of building
congestion degree and building density reached at 6.35% and 4.81%,
respectively. However, the current study also found that the importance
of three-dimensional fractal and building height was relatively low? The
possible reason was that green space is correlated with impervious
surfaces, and impervious surfaces is highly correlated with building
coverage ratio. Meanwhile, buildings with higher three-dimensional
fractal generally occupy larger base area and taller building height.
The green space, building height, and 3D fractal had occlusion effects
on urban flooding and thereby the importance of building coverage ratio
and building height became weaker.