Which hydrodynamic modeling parameter is most sensitive when simulating tidal currents in a complex estuarine environment for resource assessment?

The hydrodynamic modeling parameter that is most sensitive when simulating tidal currents in a complex estuarine environment for resource assessment is the bottom roughness coefficient, also known as Manning's n or Chezy's C, as it directly influences the frictional resistance experienced by the tidal flow and therefore significantly affects the simulated current velocities and overall tidal dynamics. Hydrodynamic modeling involves using computer simulations to predict the movement of water in a given area. Estuaries are complex environments characterized by shallow waters, irregular bathymetry (depth variations), and significant freshwater inflows, all of which influence tidal currents. The bottom roughness coefficient represents the frictional resistance between the water flow and the seabed. A higher roughness coefficient indicates greater resistance, which slows down the water flow. Inaccurate estimation of the bottom roughness coefficient can lead to significant errors in the simulated current velocities and tidal heights. This is particularly true in estuaries, where the bottom roughness can vary considerably due to the presence of different sediment types, vegetation, and other obstructions. For example, a muddy seabed will have a lower roughness coefficient than a rocky seabed. An overestimation of the bottom roughness coefficient will result in underestimation of the tidal current velocities, leading to an underestimation of the available tidal energy resource. An underestimation of the bottom roughness coefficient will result in overestimation of the tidal current velocities, leading to an overestimation of the available tidal energy resource. Therefore, accurate calibration of the bottom roughness coefficient is essential for reliable hydrodynamic modeling in estuarine environments. Calibration typically involves comparing the simulated current velocities and tidal heights with field measurements and adjusting the bottom roughness coefficient until the model results match the measurements. High-resolution bathymetric data and detailed knowledge of the seabed composition are also crucial for accurately estimating the bottom roughness coefficient. Using remote sensing techniques to map vegetation and sediment types can also improve the accuracy of the model.