Emily Burchfield

Title

Ph.D. Candidate, Environmental Engineering, Management and Policy, Vanderbilt University

Fellow, Vanderbilt Institute for Energy and the Environment

Education

M.A., Economics, University of Louvain, Louvain, Belgium
B.S., Economics, University of Louvain, Louvain, Belgium
B.A., Economics, Clemson University, Clemson, SC

Research Interests 

My research integrates survey data, geospatial data, remotely-sensed imagery, and qualitative data to identify the factors that moderate the effects of meteorological drought on surface water irrigation systems across space and time.  I am particularly interested in using quantitative tools to explore political ecological and distributional dimensions of climate change and adaptation.

For more on my research interests, visit this website:  https://ekburchfield.wordpress.com/  

Presentations 

For the most recent list of presentations and publications, please visit https://ekburchfield.wordpress.com/  

Published Papers 

§  Burchfield, E., Gilligan, J. (2016).  Dynamics of individual and collective agricultural adaptation to water scarcity.  Forthcoming in  Winter Simulation Conference 2016 Proceedings.

§  Burchfield, E., Nay, J., Gilligan, J. (2016).  Application of machine learning to prediction of vegetation health.  The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.  XLI-B2, 465-469, doi:10.5194/isprs-archives-XLI-B2-465-2016.

§  Gunda, T., Benneyworth, L., Burchfield, E. (2015).  Exploring water indices and associated parameters: A case study approach, Water Policy, 17(1), 98 – 111.

§  Nwosu, O., Hennessey, E., Burchfield, E., Barnes, S., Brinkley-Rubenstein, L., and Shields, S. (2013)  Faculty and student experiences as a model for the academy in action. In Barnes, S. L., Brinkley-Rubinstein, L., Doykos, B., and Martin, N. (Eds). Academics in Action! A Model for Community Engaged Research, Teaching, and Service.   London:  Fordham University Press.

Papers Under Review

§  Nay, J., Burchfield, E.,  Gilligan, J. (2016).  A machine learning approach to forecasting remotely sensed vegetation health.  Revised and resubmitted, Computers and Geosciences.   Check out the project website here.

§  Burchfield, E.,  Gilligan, J. (2016). Agricultural adaptation to drought in the Sri Lankan dry zone. Revised and resubmitted, Applied Geography.

Hobbies and Interests

Gardening, biking, and hiking