Coupling hydrodynamic and wave models for storm tide simulations - Yuji Funakoshi

The ADCIRC-2DDI model solves shallow water equations using finite element methods. This depth-integrated approach captures tidal dynamics and storm surge development. The model processes astronomical tides as primary forcing mechanisms. Tributary inflows contribute to water level variations. Meteorological effects include wind stress and atmospheric pressure gradients. The generalized wave continuity form ensures numerical stability. The model domain spans the East Coast, Gulf of Mexico, and Caribbean Sea. Special refinement focuses on the St. Johns River area. This comprehensive coverage enables accurate boundary condition specification.

SWAN represents third-generation spectral wave models for coastal applications. The wave action balance equation governs wave evolution processes. Wind forcing drives wave generation and growth. Sea surface elevations modify wave propagation patterns. Current conditions affect wave refraction and shoaling. The model estimates wave parameters in estuaries and nearshore zones. Wave radiation stress calculations feed into hydrodynamic simulations. This output creates the foundation for model coupling procedures.

Domain construction follows a multi-scale approach. Large-scale coverage ensures proper offshore boundary conditions. Local refinement captures riverine and estuarine details. The St. Johns River receives particular attention due to flooding concerns. Bathymetric data integration ensures accurate depth representation. Grid resolution varies based on geometric complexity. Coastal areas demand finer mesh spacing. Deep ocean regions utilize coarser elements for computational efficiency.

Uni-directional coupling transfers SWAN output to ADCIRC simulations. Wave radiation stress gradients modify momentum equations. The process occurs without feedback from hydrodynamics to waves. This simplified approach reduces computational demands. Results show substantial improvements over uncoupled simulations. Peak water levels increase by 10-15% during storm events. The methodology provides a practical option for operational forecasting. Implementation requires careful timing of data transfer between models.

Full coupling enables bidirectional information exchange. ADCIRC provides water levels and currents to SWAN. SWAN returns updated wave radiation stresses to ADCIRC. The iteration continues throughout the simulation period. This approach captures complete wave-current interaction physics. Storm tide hydrographs show modified peak timing and magnitude. Peaks decrease while troughs increase compared to uni-coupling. The interaction smooths water level variations. Computational costs rise due to iterative exchanges. Accuracy improvements justify the additional processing requirements.

Wave radiation stresses represent momentum flux from waves to currents. Breaking waves generate setup in the nearshore zone. This setup adds to astronomical tides and meteorological surge. The combined effect produces total storm tide levels. Radiation stress gradients drive additional water transport. Coastal flooding predictions improve when these effects are included. The mechanism proves especially important during hurricane landfall. Shallow water regions experience the strongest radiation stress impacts.

Calibration begins with astronomical tide validation. Harmonic constituents are adjusted to match NOS observations. Tidal phase and amplitude receive careful attention. Next, tributary inflows and meteorological effects are added. Wind stress and atmospheric pressure modify water levels. River discharge contributes to overall water balance. Finally, complete storm tide simulations incorporate all forcings. Each step builds confidence in model performance. The systematic approach isolates error sources effectively.

NOS tide gauge stations provide validation benchmarks. Water level time series show strong correlation with simulations. Peak storm tide timing matches observations closely. Magnitude differences remain within acceptable error bounds. The coupled model reduces prediction errors significantly. Statistical metrics quantify performance improvements. Root mean square errors decrease when wave effects are included. The validation supports operational deployment of the modeling system.

The St. Johns River presents unique modeling challenges. Riverine and coastal processes interact throughout the system. Upstream flows meet ocean surge propagating inland. Wind forcing on the river surface adds complexity. Model results capture these competing influences. Water levels inside the river depend on deep ocean wind forcing. Local wind effects superimpose on larger-scale patterns. The analysis reveals dominant forcing mechanisms for different river reaches.

Wind stress emerges as the primary storm forcing mechanism. Surface drag transfers atmospheric momentum to water. The effect scales with wind speed squared. Hurricane-force winds generate extreme water level responses. The St. Johns River shows particular sensitivity to wind direction. Northeasterly winds produce maximum setup conditions. Southwesterly winds cause setdown and drainage. The 122-day hindcast confirms wind dominance over other forcings. Operational forecasts must prioritize accurate wind field specification.

Atmospheric pressure variations create inverse barometer effects. Low pressure allows sea surface elevation increase. The response approximates 1 cm per millibar pressure drop. Hurricane pressure deficits reach 50-100 millibars. Resulting setup contributes 0.5-1.0 meters to storm surge. However, wind effects typically dominate pressure contributions. The St. Johns River shows minimal pressure sensitivity. Local bathymetry and geometry influence pressure response magnitude.

Tributary inflows add freshwater volume continuously. Normal discharge maintains baseline river levels. Storm rainfall increases inflow rates substantially. The combined effect raises water levels throughout the system. However, wind forcing generally supersedes inflow impacts. The relative importance varies with storm characteristics. Slow-moving systems with heavy rainfall emphasize inflow effects. Fast-moving hurricanes emphasize wind and wave contributions. Model configurations must include both mechanisms for complete accuracy.

Operational forecasting demands rapid execution times. Computational efficiency determines practical applicability. Model domains require optimization for speed and accuracy. Parallel processing capabilities accelerate calculations. Automated data ingestion systems feed current conditions. Meteorological inputs come from numerical weather prediction models. Boundary conditions update as forecasts evolve. The system must complete runs within operational time windows. Results must reach forecasters before critical decision points.

Large-scale domains capture regional ocean circulation. Coverage includes the entire East Coast and Gulf of Mexico. Coarse resolution reduces computational burden. Boundary conditions avoid artificial constraints. Local-scale domains focus on specific river systems. Fine resolution captures detailed bathymetry and topography. The St. Johns River domain demonstrates this approach. Elevation hydrographs transfer from large to local scales. Results show high accuracy despite domain separation. The strategy enables efficient operational forecasting.

National Weather Service offices require specialized tools. River Forecast Centers monitor inland flooding. Weather Forecast Offices issue coastal flood warnings. The coupled modeling system serves both functions. Storm surge predictions support evacuation decisions. River stage forecasts guide flood fighting efforts. The prototype demonstrates operational feasibility. Implementation requires training and technical support. Ongoing validation ensures continued forecast quality. The system represents a significant advancement in coastal flooding prediction.

Coupled simulations consistently show 10-15% higher peaks. This increase applies across different storm intensities. The effect results from wave radiation stress contributions. Breaking waves generate additional setup in shallow water. The magnitude depends on wave height and period. Coastal geometry influences radiation stress distribution. Both uni-coupling and full coupling produce similar peak increases. However, full coupling better captures temporal variations. The finding demonstrates wave effects cannot be ignored in storm surge modeling.

Full coupling creates distinct hydrograph changes. Peak water levels decrease slightly compared to uni-coupling. Trough levels increase during the same periods. The overall effect smooths temporal variations. Wave-current interaction drives this behavior. Currents modify wave propagation and breaking patterns. Waves alter current velocity distributions. The feedback mechanism redistributes momentum and energy. Forecasters must understand these shape changes for accurate interpretation.

The 122-day hindcast reveals forcing importance rankings. Wind stress dominates all other mechanisms. Astronomical tides provide secondary contributions. Tributary inflows rank third in most conditions. Atmospheric pressure shows minimal impact. Deep ocean wind forcing propagates into riverine reaches. Local wind effects superimpose on this base signal. The hierarchy guides model simplification for operational use. Critical forcings require accurate specification. Lesser forcings may use simplified representations without significant accuracy loss.