%0 journal article
%@ 2196-6311
%A Arlinghaus, P.,Schrum, C.,Kröncke, I.,Zhang, W.

%D 2024
%J Earth Surface Dynamics
%N 
%P 537-558 
%R doi:10.5194/esurf-12-537-2024
%T Benthos as a key driver of morphological change in coastal regions
%U https://doi.org/10.5194/esurf-12-537-2024
 
%X Benthos has long been recognized as an important factor influencing local sediment stability, deposition, and erosion rates. However, its role in long-term (annual to decadal scale) and large-scale coastal morphological change remains largely speculative. This study aims to derive a quantitative understanding of the importance of benthos in the morphological development of a tidal embayment (Jade Bay) as representative of tidal coastal regions. To achieve this, we first applied a machine-learning-aided species abundance model to derive a complete map of benthos (functional groups, abundance, and biomass) in the study area, based on abundance and biomass measurements. The derived data were used to parameterize the benthos effect on sediment stability, erosion rates and deposition rates, erosion and hydrodynamics in a 3-dimensional hydro-eco-morphodynamic model, which was then applied to Jade Bay to hindcast the morphological and sediment change for 2000–2009. Simulation results indicate significantly improved performance with the benthos effect included. Simulations including benthos show consistency with measurements regarding morphological and sediment changes, while abiotic drivers (tides, storm surges) alone result in a reversed pattern in terms of erosion and deposition contrary to measurement. Based on comparisons among scenarios with various combinations of abiotic and biotic factors, we further investigated the level of complexity of the hydro-eco-morphodynamic models that is needed to capture long-term and large-scale coastal morphological development. The accuracy in the parameterization data was crucial for increasing model complexity. When the parameterization uncertainties were high, the increased model complexity decreased the model performance.