Machine Learning Predictions of Vertical Accretion in the Mississippi River Deltaic Plain

Abstract

AbstractDeltaic 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.