Abstract
To assess the effect of sea level rise (SLR) on a 3,800-ha eelgrass meadow in Padilla Bay National Estuarine Research Reserve in Puget Sound, Washington, USA, we coupled the Marsh Equilibrium Model (MEM) with the Relative Elevation Model (REM), combining their respective strengths in simulating aboveground and belowground processes. We then modified the hybrid model to reflect an empirical relationship between eelgrass stem density and sediment accretion, making it the first model of its kind to do so. We used field data to initialize and calibrate the model, then simulated surface elevation change under various SLR and suspended sediment scenarios and tested it against a 12-year surface elevation table dataset from the site. 100-year simulations projected relative elevation loss along at least half of the elevation gradient for all SLR scenarios, and along the entire gradient for three SLR scenarios, with greater loss at higher elevations. The current suspended sediment concentration is thus insufficient for the entire eelgrass meadow to keep pace with SLR, with up to a four-fold increase required, however this presents a management conundrum in that the required sediment load may prevent eelgrass from meeting its light requirements. The main contributions of this study thus include: the novel combination of MEM and REM models, the inclusion of stem density as a factor controlling accretion, the use of a long-term data record for model initialization, calibration, and validation, and the finding that increasing sediment inputs to maintain the elevation of the habitat in the long term may be detrimental to eelgrass health in the short term.
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