Abstract
An impervious surface is considered one of main factors affecting urban waterlogging. Previous studies found that spatial pattern (composition and configuration) of impervious surfaces affected urban waterlogging. However, their relative importance remains unknown, and the scale-effect of the spatial pattern on urban waterlogging has been ignored. To move forward, our research studied the relationship between spatial patterns on the impervious surface and its subcategories (building and pavement) on urban waterlogging risk spots using Pearson correlation, partial redundancy analysis and performed at three grid scales (1 km × 1 km, 3 km × 3 km, 5 km × 5 km) and the catchment scale based on different spatial resolution land cover maps (2 m, 10 m and 30 m). We identified positively-correlated metrics with urban waterlogging risk spots, such as the composition of impervious surface (i.e., total impervious surface, building, pavement) and the aggregation metric of the total impervious surface at most scales, as well as two negatively correlated metrics (i.e., proximity metric of building, fragmentation metric of total impervious surface). Furthermore, the total variance of urban waterlogging risk spots explained by the spatial pattern of the total impervious surface and its subcategories increased with studied grid and catchment scales while decreasing from a fine to a coarse resolution. The relative contribution of the impervious surface composition and configuration to the variation of urban waterlogging risk spots varied across scales and among impervious surface types. The composition contributed more than the configuration did for the total impervious surface at both grid and catchment scales. Similar to total impervious surface, the composition of buildings was more important than its configuration was at all the grid scales, while the configuration of buildings was more important at the catchment scale. Contrary to the total impervious surface, the configuration of pavement at both the grid and catchment scales mattered more than their compositions did. Furthermore, the composition of the building was more important than that of pavement, but its configuration mattered less. Our study could provide a multi-scale landscape perspective with detailed suggestions for controlling the area of impervious surface and optimizing its spatial configuration in urban waterlogging risk mitigation and urban planning.
Highlights
Urban waterlogging, a representative type of urban flooding [1,2], refers to the phenomenon in which a rainstorm or a short-term period of heavy rain surpasses the capacity of the urban drainage system, which results in a waterlogging disaster [3]
The total variance of urban waterlogging risk spots explained by the spatial pattern of the impervious surface increased with the studied grid and catchment scales, while it decreased with the spatial resolutions
At a 2 m spatial resolution (Model 1), the composition and configuration of the impervious surface could explain 5.0–48.1% of the variations of urban waterlogging risk spots at different spatial scales, and the explanatory power of the model increased from the 1 km × 1 km to the 5 km × 5 km grid scale and to the catchment scale
Summary
A representative type of urban flooding [1,2], refers to the phenomenon in which a rainstorm or a short-term period of heavy rain surpasses the capacity of the urban drainage system, which results in a waterlogging disaster [3]. Due to global climate change and rapid urbanization, urban waterlogging has become a serious problem in urban areas worldwide [4,5]. This has resulted in socio-environmental problems such as property damage, traffic paralysis, water pollution and economic losses [6,7,8], especially in rapidly developing countries [3]. The influence mechanisms behind urban waterlogging, as a premise of taking actions, have not yet been fully understood [13,14]. Both the composition and configuration (i.e., spatial pattern) of impervious surfaces were found to affect urban waterlogging [15], their relative importance remains unknown, and the scale-effect of the influence is unknown
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