Odink, S.J.1, Willemsen P.W.J.M.1,2,3, Smits, B.3, Borsje, B.W.1, Hulscher, S.J.M.H.1
Nature-based coastal protection solutions such as salt marshes are able to complement conventional solutions. Salt marshes are able to grow with sea level rise by actively trapping sediments and creating belowground biomass, and their wave attenuating capacity is proven. Furthermore, salt marshes are widely recognized as pristine ecosystems that provide habitat to a unique and wide range of flora and fauna. Their possible use as a nature-based way of flood protection plus the recognition of the ecological value of these ecosystems has led to multiple salt marsh creation and restoration projects. However, salt marshes are also known as dynamic ecosystems, and significant changes in total covered area over time scales of decades have been observed in the past (Van der Wal et al., 2008). Both, growth and retreat have been registered at marshes in close proximity (a few kilometres only). Recent field work and model results suggest that the magnitude of (short term) hydro- and morphodynamics may be indicators for the long-term marsh growth and retreat. A better understanding of the physical processes driving the long-term evolution of these ecosystems may be valuable knowledge for efficient coastal management.
A dynamic vegetation model setup in Python was coupled to a hydro- and morphodynamic model (D-flow Flexible Mesh) in order to unravel the long-term marsh dynamics. The dynamic vegetation model consists of a combination of two existing vegetation modelling approaches. Establishment of pioneering vegetation was modelled by the Windows of Opportunity theory (Poppema, 2019). Growth and decay of vegetation was based on the population dynamics theory. For each grid cell, the vegetation was updated every several timesteps, depending on the hydro- and morphodynamic conditions in that grid cell. The relative contribution of wind waves and tides influences the cross-shore development of the profile, whereas the rate of actual establishment of vegetation affects the lay-out of the salt marsh.
Figure 1 Modelled salt marsh evolution. From left to right: initial bathymetry, vegetation and ecology after 40 years, vegetation and ecology after 80 years
Poppema, D.W., Willemsen, P.W.J.M., de Vries, M.B., Zhu, Z., Borsje, B.W., Hulscher, S.J.M.H. (2019). Experiment-supported modelling of salt marsh establishment. Ocean & Coastal Management, 168, 238-250. https://doi.org/10.1016/j.ocecoaman.2018.10.039.
Van der Wal, D., Wielemaker-Van den Dool, A., & Herman, P. M. J. (2008). Spatial patterns, rates and mechanisms of saltmarsh cycles (Westerschelde, The Netherlands). Estuarine, Coastal and Shelf Science, 76(2), 357–368. https://doi.org/10.1016/j.ecss.2007.07.017