Abstract
The assessment of populations affected by urban flooding is crucial for flood prevention and mitigation but is highly influenced by the accuracy of population datasets. The population distribution is related to buildings during the urban floods, so assessing the population at the building scale is more rational for the urban floods, which is possible due to the abundance of multi-source data and advances in GIS technology. Therefore, this study assesses the populations affected by urban floods through population mapping at the building scale using highly correlated point of interest (POI) data. The population distribution is first mapped by downscaling the grid-based WorldPop population data to the building scale. Then, the population affected by urban floods is estimated by superimposing the population data sets onto flood areas, with flooding simulated by the LISFLOOD-FP hydrodynamic model. Finally, the proposed method is applied to Lishui City in southeast China. The results show that the population affected by urban floods is significantly reduced for different rainstorm scenarios when using the building-scale population instead of WorldPop. In certain areas, populations not captured by WorldPop can be identified using the building-scale population. This study provides a new method for estimating populations affected by urban flooding.
Highlights
The frequency and intensity of flooding are increasing due to climate change [1,2]
Population mapping is key for disaster assessment calculation, with higher-resolution population datasets more accurately estimating the size and spatial distribution of affected populations [8], which greatly improves the rationality of flood-related decision-making [9]
point of interest (POI) type and WorldPop population grid to establish the urban flooding, this study assessed the population affected by urban floods using population mapping building function population distribution
Summary
According to data from EM-DAT, floods accounted for 49% of global disasters and 68% of the affected population in 2019 [3]. Floods have become the main natural disaster in many cities, which are characterized by a large population and rapid urbanization, posing serious threats to human life, production, and social and economic activities [6]. Accurate flood risk assessments for cities are vital for formulating local disaster prevention policies. Population is the most important flood-hit object; accurate population-based spatial distribution information is an important basis for disaster prevention and mitigation [7]. Population mapping is key for disaster assessment calculation, with higher-resolution population datasets more accurately estimating the size and spatial distribution of affected populations [8], which greatly improves the rationality of flood-related decision-making [9]. Common population datasets include census data provided by the government, with administrative divisions as statistical units; census data have a long update cycle and suffer from the modifiable areal unit problem (MAUP)
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