Understanding the Eco‐Geomorphologic Feedback of Coastal Marsh Under Sea Level Rise: Vegetation Dynamic Representations, Processes Interaction, and Parametric Sensitivity. Issue 11 (24th November 2020)

• Record Type: Journal Article
• Authors: Zhang, Yu; Rowland, Joel C.; Xu, Chonggang; Wolfram, Phillip J.; Svyatsky, Daniil; Moulton, J. David; Cao, Zhendong; Marani, Marco; D'Alpaos, Andrea; Pasqualini, Donatella
• Is Part Of: Journal of geophysical research. Volume 125:Issue 11(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-24
• Subjects: coastal marsh -- eco‐geomorphologic model -- landscape evolution | vegetation biomass -- sea‐level rise -- vulnerability; Geomorphology -- Periodicals; 551.3
• DOI: 10.1029/2020JF005729
• Languages: English
• ISSNs: 2169-9003

Abstract

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. Key Points: Marsh platform relief increases with sea‐level rise (SLR) rate The nonlinear Spartina scheme is the most resilient scheme, and the linear Spartina scheme predicts the lowest unvegetated‐vegetated ratio Models become more sensitive to vegetation‐related parameters for increasing SLR rates