M.V. Bilskie, S.C. Hagen, P. Bacopoulos, A. Thomas, P. Hovenga, (2014) “Sea level rise and tidal hydrodynamics in the Indian River Lagoon, Florida.” 34th International Conference on Coastal Engineering, Seoul, Korea, June 15-20, 2014.
Introduction: The 156 mile Indian River Lagoon (IRL), located on the Atlantic coast, is a collection of three individual lagoons: Mosquito Lagoon, Banana River, and the Indian River, including Cape Canaveral. The lagoon consists of five narrow-width inlets (from north to south): Ponce de León, Sebastian, Fort Pierce, Saint Lucie, and Jupiter. The system also contains a chain of causeways and tens of thousands of acres of dense submerged aquatic vegetation (SAV) that combine to constrict and dampen tidal flow and flushing of the lagoon. The offshore tide range is on the order of one meter, while the lagoon is micro-tidal, with peak tide range in some areas as low as a few centimetres as found in Haulover Canal (70 m width) that connects Mosquito Lagoon to the Indian River. The IRL system is an important resource to the region, generating an economic value of about $3.7 billion and 15,000 jobs and offers recreational activities to more than 11 million people per year (St. Johns River Water Management District, 2013).
Methodology: To understand the complex physical processes of the lagoon system and to define what causes the major tidal damping, an unstructured, two-dimensional, triangular finite element hydrodynamic model is constructed and validated for astronomic tides. Model resolution varies from several kilometers in the deep Atlantic Ocean to as low as 10 m within the lagoon. Bathymetry within the lagoon is incorporated into the model from most-recent high resolution bathymetric surveys and soundings provided by the Kennedy Space Center Ecological Services group. Bottom friction in terms of Manning’s n is parameterized based on offshore sediment types and land use land cover (LULC) maps. Several sources of LULC maps are jointed to provide an accurate map of bottom soil types, SAV, and mangroves. The model is forced with eight tidal constituents (O1, K1, P1, Q1, M2, S2, N2, K2) along the open ocean boundary derived from Oregon State University’s TPXO7.2 global ocean model (Egbert et al., 1994; Egbert and Erofeeva, 2002).
The model is used to examine and determine the major contributing factors that cause the low tide range within the IRL system. We hypothesize that the limited and narrow inlets, shallow depths, and numerous causeways (i.e., geometry of the system), not the dense SAV, cause the low tide range. We performed several experiments to prove or disprove our hypothesis; firstly, we carefully construct the model to describe all inlets and inter-connections with high resolution representation to examine the geometric influences. Additionally, we alter bottom roughness to reflect conditions of minimal SAV and high seagrass coverage. Finally, the model is modified by removing the causeways, returning the lagoon to its natural shoreline configuration.
Furthermore, we also explore how sea level rise (SLR) may alter the tidal hydrodynamics of the system. Additional experiments are performed using regional projections of SLR. The model experiments are compared to each other and to the control (validation) simulation. Tidal harmonics are decomposed from the full tide signal and analysed as well as the dominated overtides of the M2 harmonic, M4 and M6, which are generated from shallow water continuity and local accelerative advection, and bottom friction, respectively (Parker, 1991).
Discussion and Conclusions: Comparing modeled to measured astronomic tides via harmonic analysis reveals high model skill, with the average root mean square error (RMSE) of 4.1 cm and standard deviation of 3.2 cm across the 24 tide gage stations. Results show that the shoreline configuration and system geometry are the dominating factors that dampen the tide range. Bottom friction plays a secondary role in the phase (i.e., timing) of the tide, and proves important in reproducing tide signals when tide range is less than 5 cm. Under SLR scenarios, the tide range offshore is increased, but the IRL system introduced many non-linearity’s; the tide range does not increase by the amount of SLR. Additionally, we conclude that to appropriately simulate future conditions of the IRL, we must include overland regions in the model to capture the possible formation of new inlets and overtopping of barrier islands due to SLR.
Egbert, Bennett, & Foreman (1994): TOPEX/POSEIDON tides estimated using a global inverse model. Journal of Geophysical Research: Oceans, 99(C12), 24821-24852. doi: 10.1029/94JC01894
Egbert & Erofeeva (2002): Efficient Inverse Modeling of Barotropic Ocean Tides. Journal of Atmospheric and Oceanic Technology, 19(2), 183-204. doi: 10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2
Parker (1991): The Relative Importance of the Various Nonlinear Mechanisms in a Wide Range of Tidal Interactions (Review). New York, New York, John Wiley and Sons.
St. Johns River Water Management District. (2013). The Indian River Lagoon: An estuary in distress. Retrieved 9-27, 2013