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Understanding the Eco‐Geomorphologic Feedback of Coastal Marsh Under Sea Level Rise: Vegetation Dynamic Representations, Processes Interaction, and Parametric Sensitivity

Yu Zhang,J. Rowland,Chonggang Xu,P. Wolfram,D. Svyatsky,J. D. Moulton,Zhendong Cao,M. Marani,A. D’Alpaos,D. Pasqualini

Published 2020 in Journal of Geophysical Research: Earth Surface

ABSTRACT

A growing number of coastal eco‐geomorphologic modeling studies have been conducted to understand coastal marsh evolution under sea‐level rise (SLR). Although these models quantify marsh topographic change as a function of sedimentation and erosion, their representations of vegetation dynamics that control organic sedimentation differ. How vegetation dynamic schemes contribute to simulation outcomes is not well quantified. Additionally, the sensitivity of modeling outcomes to parameter selection in the available formulations has not been rigorously tested to date, especially under the influence of an accelerating SLR. In this study, we used a coastal eco‐geomorphologic model with different vegetation dynamic schemes to investigate the eco‐geomorphologic feedbacks of coastal marshes and parametric sensitivity under SLR scenarios. We found that marsh platform relief increased with SLR rate. The simulations with different vegetation schemes exhibited different spatial‐temporal variations in elevation and biomass. The nonlinear Spartina scheme presented the most resilient prediction with generally the highest marsh accretion and vegetation biomass, and the least elevation relief under SLR. But the linear Spartina scheme predicts the lowest unvegetated‐vegetated ratio. We also found that vegetation‐related parameters and sediment diffusivity, which were not well measured or discussed in previous studies, were identified as some of the most critical parameters. Additionally, the model sensitivity to vegetation‐related parameters increased with SLR rates. The identified most sensitive parameters may inform how to appropriately choose modeling representations of key processes and parameters for different coastal marsh landscapes under SLR and demonstrate the importance of future field measurements of these key parameters.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    Journal of Geophysical Research: Earth Surface

  • Publication date

    2020-10-31

  • Fields of study

    Geography, Environmental Science

  • Identifiers
    DOI 10.1029/2020JF005729
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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