A coupled, two-dimensional hydrodynamic-marsh model with biological feedback
A spatially-explicit model (Hydro-MEM model) that couples astronomic tides and Spartina alterniflora dynamics was developed to examine the effects of sea-level rise on salt marsh productivity in northeast Florida. The hydrodynamic component of the model simulates the hydroperiod of the marsh surface driven by astronomic tides and the marsh platform topography, and demonstrates biophysical feedback that non- uniformly modifies marsh platform accretion, plant biomass, and water levels across the estuarine landscape, forming a complex geometry. The marsh platform accretes organic and inorganic matter depending on the sediment load and biomass density which are simulated by the ecological-marsh component (MEM) of the model and are functions of the hydroperiod. Two sea-level rise projections for the year 2050 were simulated: 11 cm (low) and 48 cm (high). Overall biomass density increased under the low sea-level rise scenario by 54% and declined under the high sea-level rise scenario by 21%. The biomass-driven topographic and bottom friction parameter updates were assessed by demonstrating numerical convergence (the state where the difference between biomass densities for two different coupling time steps approaches a small number). The maximum coupling time steps for low and high sea-level rise cases were calculated to be 10 and 5 years, respectively. A comparison of the Hydro-MEM model with a parametric marsh equilibrium model (MEM) indicates improvement in terms of spatial pattern of biomass distribution due to the coupling and dynamic sea-level rise approaches. This integrated Hydro-MEM model provides an innovative method by which to assess the complex spatial dynamics of salt marsh grasses and predict the impacts of possible future sea level conditions.
Digital Object Identifier (DOI)
Alizad, Hagen, S. C., Morris, J. T., Bacopoulos, P., Bilskie, M. V., Weishampel, J. F., & Medeiros, S. C. (2016). A coupled, two-dimensional hydrodynamic-marsh model with biological feedback. Ecological Modelling, 327, 29–43. https://doi.org/10.1016/j.ecolmodel.2016.01.013