Race, Income Inequality, and Impervious Surfaces in Relation to Flooding Associated with Hurricane Harvey
Abstract
Differential vulnerability is a concept that suggests that certain demographic groups - the poor, less educated, or minorities - are likely to be more impacted by climate extremes such as floods owing to their higher sensitivity and lower adaptive capacity. Differential exposure represents the concept that these same groups may be more highly exposed to flood events by virtue of their residing in less desirable, low-lying neighborhoods with higher percentages of impervious surface cover. This paper tests the hypothesis that poor communities of color were differentially exposed to flood risks in the aftermath of Hurricane Harvey, which struck Houston, Texas in August 2017. We explore the spatial relationship among census tracts with high percentages of low income communities of color, those with high percentages of impervious surface, and those most impacted by floods. We incorporat datasets disseminated by the NASA Socioeconomic Data and Application Center (SEDAC) - the Global Man-made Impervious Surface (GMIS) data set and the U.S. Census Grids 2010 - together with the American Community Survey (ACS) 2011-2015 and flood extent and depth data from FEMA. Preliminary analysis suggests that predominantly non-white neighborhoods have higher percentages of impervious surface cover, but that impervious surface cover is negatively correlated with flood risk. This paper will situate these findings in the context of a larger body of research exploring differential exposure to flood risks during Hurricanes Katrina and Sandy, as well as differential exposure to extreme heat in urban environments in Houston and beyond.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFMIN31F..01D
- Keywords:
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- 1910 Data assimilation;
- integration and fusion;
- INFORMATICS;
- 1922 Forecasting;
- INFORMATICS;
- 4313 Extreme events;
- NATURAL HAZARDS;
- 4331 Disaster relief;
- NATURAL HAZARDS