Wind-Driven Setup, Roughness, and Drag over a Depth- and Fetch-Limited Coastal Estuary

Abstract

The understanding of the momentum exchange across the air-sea interface is critical to accurately model and forecast wind speed, wave properties, setup, and circulation. This is particularly true for a restricted estuary, such as the Indian River Lagoon (IRL), where wind is the driving force behind water movement due to the limited number of direct ocean connections. Current atmospheric and hydrodynamic models use formulations of the drag coefficient and/or roughness length based on open ocean studies in which wave properties are not considered. This dissertation develops two parameterizations – one for wind setup and one for the drag coefficient – both based on wind speed, fetch, and mean water depth. Various methods of generating an hourly time series to force three wind setup parameterizations are explored using a combination of in-situ water level gauges and local wind observations. Each parameterization and forcing is tuned to IRL water level observations using a least squares fit. The best performing pair consists of a modified version of the well-known Zuiderzee formula forced by a 12-hour wind run based hourly time series. This optimized parameterization is currently being used operationally to generate ensemble setup forecasts, designed to guide the National Weather Service (NWS) in identifying potentially significant setup events that warrant high resolution hydrodynamic simulations. Wind-driven waves have been shown to play a role in determining wind stress in coastal areas and over lakes, but research is lacking on water bodies with short fetches (<10 km) like those found on the IRL. The approach here is to parameterize surface roughness based on wave age and wave steepness. Non-dimensional approaches to calculate wave energy and peak frequency, from which the needed wave properties could be determined, were evaluated for the IRL. The non-dimensional roughness was then related to wave age and wave steepness using power laws. Drag coefficients were calculated from the parameterized roughness values, with no significant difference between the wave age and wave steepness predictions for the IRL. However, the wave-based data show a slightly decreasing drag coefficient with increasing wind speed, unlike current model parameterizations. The work presented here indicates that the drag coefficient over the IRL is more dependent on fetch than water depth. Therefore, a new drag coefficient formulation, dependent on both wind speed and fetch, is proposed and evaluated for the IRL. The lack of wave instrumentation to provide the necessary observations for the wind stress research led to the development of a method to extract wave properties from video. The technique proposed here does not rely on a series of ground control points – objects with a known physical location in relation to the camera – as used in previous studies. By not requiring ground control points, the system is inexpensive, portable, and easy to deploy, allowing the collection of wave data from various locations along the estuary. Significant wave heights and peak frequencies were validated with a wave gauge located in the camera’s field of view. The proposed method is robust and the errors are consistent with other video-based methods that use multiple cameras and ground control points.