Undergraduate Research Assistant in Flood Risk Mapping- GEOSCI
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About this role
JOB DESCRIPTION AND POSITION REQUIREMENTS
The department of Geosciences is searching for a undergraduate research assistant.
Over the past 30 years, flooding has been the second largest cause of weather-related fatalities in the USA. This can be particularly dangerous in urban centers where there are large concentrations of people and important infrastructure. As climate change alters precipitation patterns, extreme rainfall may become more likely in some locations, including the USA’s Mid-Atlantic region. Baltimore, MD, is regularly impacted by flooding, which damages property, disrupts transit, and affects peoples' livelihoods. The risks posed by flooding are often inequitably distributed and can have greater effect in places which are economically disadvantaged or topographically low-lying.
This research seeks to identify and map high flood risk zones in the city of Baltimore. Our team has created an urban flooding model which simulates Baltimore’s land surface and stormwater infrastructure. When the model is run, we can visualize places where flooding is occurring. The goal of this work is to build a flood risk map of the city, linking places where flooding occurs, who is exposed, and who is vulnerable.
The undergraduate research assistant will contribute to this effort by searching for risk data across the city, using GIS tools to map out risk factors, and integrating flood modeling results. Through this work, the student will gain hands on experience with applied climate risk analysis, natural hazard mapping, and environmental inequity research.
Desired skills and interests
Applicants should demonstrate interest or experience in the following areas.
- An interest in climate science, environmental studies, data science, or related fields. The student should be curious and ready to learn more about risk analysis!
- Prior experience with QGIS or ArcGIS, specifically importing and visualizing spatial datasets.
- Basic coding experience. The student should be prepared to learn to clean data and configure datasets for import.