March 26, 2024

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Etienne Chenevert Coauthored a Paper in the Journal of Geophysical Research

Etienne Chenevert (California) coauthored the paper, “Machine Learning Predictions of Vertical Accretion in the Mississippi River Deltaic Plain,” in the Journal of Geophysical Research: Earth Surface on March 14, 2024.

Etienne’s coauthor was Douglas A. Edmonds of the Department of Earth and Atmospheric Sciences at Indiana University-Bloomington.

Etienne is a Staff Professional Scientist who recently earned an MS in Geological and Earth Sciences from Indiana University-Bloomington. In his research, he used GIS and machine learning to better understand the processes that drive deposition on the coastal wetlands of Louisiana.

The Journal of Geophysical Research: Earth Surface publishes original research articles on the physical, chemical, and biological processes that affect the form and function of the surface of the solid Earth over all temporal and spatial scales. The journal is published by the American Geophysical Union.

The American Geophysical Union is an international nonprofit association supporting an inclusive community of Earth and space scientists and partners dedicated to discovery and solutions to societal challenges. In addition to being a scholarly publisher, the association convenes virtual and in-person events, and provides career support.


Deltaic landscapes consist of vast wetland systems that rely on sedimentation to maintain their elevation and ecological communities against relative sea-level rise. In the Mississippi River Deltaic plain, rising relative sea level and anthropogenic activities are causing land loss that will continue unless vertical accretion of sediment on the wetland surface is enough to fill the accommodation space. Even though the fate of the Mississippi Deltaic plain is tied directly to vertical accretion, there is not yet a clear understanding of the system-wide controls on this process. Here, we investigate vertical accretion in coastal Louisiana using a data set of 266 stations from the Coastwide Reference Monitoring System (CRMS). Using linear regression models, we analyze vertical accretion in freshwater-intermediate, brackish, and saline marsh communities. Integrating results from these models into a Gaussian Process regression model, we predict controls on vertical accretion rates across the deltaic plain. Consistent with previous studies, our results suggest that tidal amplitude and flood depth are critical controls on vertical accretion. These effects are additive and marshes with high tidal amplitudes and flood depths experience the most vertical accretion. Interestingly, the normalized difference vegetation index is found to be important for predicting vertical accretion, but not because of an increase in biomass production, but because it records unique marsh communities and flooding regimes. This study emphasizes the importance of incorporating marsh specific information into predictive models for the vertical accretion of coastal wetlands and that better predictions of wetland accretion probably require denser observational data.

More Information

Learn more about the paper: Machine Learning Predictions of Vertical Accretion in the Mississippi River Deltaic Plain
Learn more about the Journal of Geophysical Research: Earth Sciences.
Learn more about the American Geophysical Union.
For more information on machine learning or vertical accretion, contact Etienne at This email address is being protected from spambots. You need JavaScript enabled to view it..
Learn more about Etienne: Etienne Chenevert | LinkedIn