The social and economic impact of urban flooding is becoming more severe in the United States over time. Urban areas are mostly vulnerable to flash floods because of the impervious surface, which increases the surface runoff. More than 80 percent of people live in urban areas in the United States, and they are at higher risk of urban flooding. Although many urban areas have a higher risk of urban flooding, there is still a significant knowledge gap of understanding between the minority's and nonminority's vulnerability to urban floods. Therefore, using Birmingham, Alabama, as a study area, this research designs a quantitative thematic mapping method to assess the flood risks of urban population and buildings. In this research, census data was used to assess urban residents' vulnerability to flooding using thematic mapping method – location quotient (LQ) and compare it with the widely used social vulnerability index. The findings suggest that the aggregation of White populations is much higher compared to minorities. This research also developed a flood risk model using integrated GIS and cartographic approach considering different environmental factors that influence the urban floods. This study found that the Valley Creek area is the highest flood risk zone in Birmingham, and has the highest percentage of residential (i.e., 56.14 %) and commercial (i.e., 75.34 %) buildings located in very high flood risk areas. The decennial census data from 1990 to 2015 was used to examine whether vulnerable population groups aggregated more in the flooding areas or moved away from Birmingham's flooding areas in the past thirty years. The findings of this research indicate that most minorities are aggregating more in the floodplain areas, whereas the non-minorities are moving away from the flooding regions.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-5017 |
| Date | 25 November 2020 |
| Creators | Hossain, Mohammad Khalid |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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