Spatial distribution of water level impacting back-barrier bays

Abstract

Abstract. Water level in semi-enclosed bays, landward of barrier islands, is mainly driven by offshore sea level fluctuations that are modulated by bay geometry and bathymetry, causing spatial variability in the ensuing response (transfer). Local wind setup can have a complementary role that depends on wind speed, fetch, and relative orientation of the wind direction and the bay. Bay area and inlet geometry and bathymetry primarily regulate the magnitude of the transfer between open ocean and bay. Tides and short-period offshore oscillations are more damped in the bays than longer-lasting offshore fluctuations, such as a storm surge and sea level rise. We compare observed and modeled water levels at stations in a mid-Atlantic bay (Barnegat Bay) with offshore water level proxies. Observed water levels in Barnegat Bay are compared and combined with model results from the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system to evaluate the spatial structure of the water level transfer. Analytical models based on the dimensional characteristics of the bay are used to combine the observed data and the numerical model results in a physically consistent approach. Model water level transfers match observed values at locations inside the bay in the storm frequency band (transfers ranging from 50 %–100 %) and tidal frequencies (10 %–55 %). The contribution of frequency-dependent local setup caused by wind acting along the bay is also considered. The wind setup effect can be comparable in magnitude to the offshore transfer forcing during intense storms. The approach provides transfer estimates for locations inside the bay where observations were not available, resulting in a complete spatial characterization. An extension of the methodology that takes advantage of the ADCIRC tidal database for the east coast of the United States allows for the expansion of the approach to other bay systems. Detailed spatial estimates of water level transfer can inform decisions on inlet management and contribute to the assessment of current and future flooding hazard in back-barrier bays and along mainland shorelines.

The Effect of Harbor Developments on Future High‐Tide Flooding in Miami, Florida

“…A hydrodynamic model would be required to substantiate these observations (see e.g., Pelling et al., 2013), and it is possible that some frictional factors that we are unable to ascertain (such as changed bed morphology or altered sea‐grass extent) are also influencing tidal amplitudes. The hardening of the coast‐line and its influence on tidal reflection (Talke & Jay, 2020), wind driven circulation and water level setup, and interaction between multiple inlets (Aretxabaleta et al., 2019) may also be important for tides and non‐tidal residuals.…”

“…Because no inlet existed in 1900, even an attenuated tidal forcing through the inlet has likely impacted tides and HTF within Northern Biscayne Bay. The spatial pattern of tidal range changes caused by inlet creation, dredging and modification, and the combined effect of all changes, can only be assessed using analytic or numerical modeling approaches (Aretxabaleta et al, 2017(Aretxabaleta et al, , 2019Wong & DiLorenzo, 1988). Such an analysis is beyond the scope of this effort, and is left for future research.…”

A novel statistical approach to predict seasonal high tide flooding

“…It is important to note that the assessment of HTF flooding occurrence as presented here is dependent on relating tide gauge point observations to empirically derived flood thresholds (Sweet et al, 2018). Spatial variations in coastal water level variability is in some places significant, especially in enclosed bays and estuaries (Aretxabaleta et al, 2019), and thus flooding even a short distance away from the tide gauge may not be accurately reflected by these observations. The position of the tide gauge also may not well capture freshwater contributions (i.e., associated with precipitation, runoff, and flow from rivers or canals).…”

Spatial Variability in Salt Marsh Drainage Controlled by Small Scale Topography

“…A robust quantification of such fluxes allows us to predict which parts of a salt‐marsh system will erode and which parts will grow; this information can be employed, for instance, to design effective interventions along the shoreline, and forecast the location and timing of new land creation through sediment accumulation (e.g., Tas et al., 2022; Winterwerp et al., 2020). Additionally, UAVSAR observations may be applied to improve the accuracy of numerical models to simulate hydrodynamics within vegetated areas, which is fundamental to quantify the impact of future extreme events on coastal communities (e.g., Aretxabaleta et al., 2019; Temmerman et al., 2022). A better understanding of how water levels are impacted by vegetation is important to limit the economic impact of coastal hazards and increase the capability of communities and coastal economies to recover (e.g., Goreau & Hilbertz, 2005).…”