Mapping inequality and vulnerability to urban flooding in Twin City Metro Area, US: A combined approach by citizen science survey and a simplified inundation model

Abstract:

Urban pluvial flooding is a global challenge that is frequently caused by the lack of drainage capacity in cities. Neither federal nor state government track urban flooding as it occurs or over time. There’s very little empirical information on urban flooding at the fine-scale to inform social inequality and vulnerability. In this study, we explored different approaches including both a citizen science survey and the flood model simulation results to map social inequality in exposure to urban flooding risk in the Twin City Metro Area, US. First, we’ve created an online survey questionnaire by asking people to recall the urban flooding locations and how flooding events impacted their daily life. This survey was launched at 2019 Minnesota State Fair and successfully collected ~350 responses during a week. Survey data shows that census tracts in the Twin City Metro Area with higher household income tend to have fewer floods. Further analyses with a high-resolution digital elevation model (1-meter DEM) also reveal that disadvantaged neighborhoods tend to have lower elevations and thus may be more vulnerable to flooding. Additionally, a simplified urban flooding inundation model has been developed for the northeast Minneapolis watershed. Combining a 1D hydraulic drainage network model (SWMM) and theory of “Storage cell method”, this simplified model aims to rapidly estimate the final/maximum flood extent and depth by spreading flood volumes to topographic depressions in an urban catchment identified by the high-resolution DEM. Prelim results show that block groups with lower household income (<$61,000) tend to suffer more from urban flooding events. The inundation area ratio (i.e, simulated inundation area divided by the whole block group area) is 5.3% for lower-income block groups vs 1.9% for higher-income block groups under a 10-year rainfall event scenario.