The equation for seagrass biomass in a given cell is computed as shown in the below table, assuming photosynthesis, excretion, mortality and excretion. Nutrient uptake is not included in the simulation in SCERM v1 since Hillman et al. (1995) highlight that light, salinity and temperature were the dominant drivers, with some minor correlation with phosphate levels, however the stoichiometry is known and may be included in future iterations. The effective coverage area (i.e. Leaf Area Index, HALOLAI of the meadow) can be computed based on the scaling expression in Baird et al. (2016); although this is not directly used in the photosynthesis calculation, it is useful output for comparing with field data.

Growth is calculated in response to light, but also sensitive to salinity and temperature:

Photosynthesis-irradiance relationships for Halophila have been estimated by Ralph and Burchett (1995) who found photo-inhibition occurring at modest light intensities. The Steele (1962) equation is therefore suggested as the most appropriate. Light extinction can also occur over the meadow depth, although for Halophila this is assumed to be relatively small due to the small leaf structure. The salinity effect on photosynthesis has been reported by Ralph (1998b) and Hillman et al. (1995).

Respiration, excretion and mortality are also commonly simulated with typical first-order rate coefficients for each:

The seagrass-sediment-light (SSL) feedback has been identified as a potentially important driver determining meadow persistence (Adams et al., 2016), as depicted schematically for a numerical model in Figure 8. This requires the connection between sediment resuspension and seagrass presence to be made, however, the complete feedback loop has rarely been reported in aquatic models to date. The model implemented for SCERM focuses on Halophila and accounts for the feedback by including ability to simulate the link between: a) above ground biomass (l“DYÀE) and shear stress, b) below ground biomass (l“DYFE), and c) the amount of resuspension and light.

To simulate the SSL feedback, drag is increased in proportion with the above ground biomass:

where \(C_d\) is the base drag coefficient for a numerical cell based on its sediment material properties, and the above ground fraction is a user definable constant fraction of total biomass. The default value of \(C_{D_{bottom}}\)is stored in the hydrodynamic driver model and influences the local momentum budget; therefore the 2nd term of the RHS is passed to the host model calling AED2. The critical shear stress for resuspension is also increased based on the below ground biomass:

where \(\tau_0\) is critical shear stress for resuspension, computed based on a minimum value (reflecting bare sediment), and linear coefficient linked to biomass. As HI increases, the concentration of SS in the local domain will reduce, thereby improving the overall light climate. Note that in the model, presence or absence of HALO in a given cell can be configured be reading in an appropriate distribution map.