In June 2024, I successfully defended my dissertation and completed my Ph.D. in Geospatial Analytics at North Carolina State University. I am excited to build on the final data chapter of my dissertation—where we used machine learning to classify surface water from high resolution satellite imagery—as a Postdoctoral Scholar in the GAEC lab.

In July of 2024, I published the second chapter of my dissertation: Projecting Surface Water Area Under Different Climate and Development Scenarios . In this paper, we built on the results of Gaines et al. (2022) where we found that a combination of climate and land cover variables in a Mixed Effect Random Forest (MERF) model produced the most accurate estiamations of surface water area. In this study, we projected surface water area in the Southeast through 2099 under a comnination of emissions and development scenarios that are similar to the storylines of the Shared Socioeconomic Pathways developed for the most recent Intergovernmental Panel on Climate Change report. We found that the most extreme scenario projected the most extreme changes in surface water area, both on average across the study area and at the watershed scale.

In March of 2022, I published the first chapter of my dissertation: Effects of Climate and Anthropogenic Drivers on Surface Water Area in the Southeastern United States . In this paper, we explore the differences in the ability of three different groups of drivers to estimate surface water change in the Southeast - climate, anthropogenic, and the combination of the two. The Southeast is an interesting study area both because of its high population growth rate - the highest of any region in the country! - and because of its large amount of land cover change. It also experiences a wide range of effects from climate change, with a hurricane season in the late summer and early fall, increased rainfall in the winter, and increasingly regular dry spells and even droughts in the summer. The data-driven models we developed use a combination of statistical and machine learning techniques to indicate which group of drivers explains the most amount of variance in surface water across the region, as well as which individual variables are the most important in estimating surface water area.