The city of Pascagoula and its coastal areas along the United States Gulf Coast have experienced many catastrophic hurricanes and were devastated by high storm surges caused by Hurricane Katrina (August 23 to 30, 2005). The National Hurricane Center reported high water marks exceeding 6 meters near the port of Pascagoula with a near 10-meter high water mark recorded near the Hurricane Katrina landfall location in Waveland, MS. Although the Pascagoula River is located 105 km east of the landfall location of Hurricane Katrina, the area was devastated by storm surge-induced inundation because of its low elevation. Building on a preliminary finite element mesh for the Pascagoula River, the work presented herein is aimed at incorporating the marsh areas lying adjacent to the Lower Pascagoula and Escatawpa Rivers for the purpose of simulating the inland inundation which occurred during Hurricane Katrina. ADCIRC-2DDI (ADvanced CIRCulation Model for Shelves, Coasts and Estuaries, Two-Dimensional Depth Integrated) is employed as the hydrodynamic circulation code. The simulations performed in this study apply high-resolution winds and pressures over the 7-day period associated with Hurricane Katrina. The high resolution of the meteorological inputs to the problem coupled with the highly detailed description of the adjacent inundation areas will provide an appropriate modeling tool for studying storm surge dynamics within the Pascagoula River. All simulation results discussed herein are directed towards providing for a full accounting of the hydrodynamics within the Pascagoula River in support of ongoing flood/river forecasting efforts. In order to better understand the hydrodynamics within the Pascagoula River when driven by an extreme storm surge event, the following tasks were completed as a part of this study: 1) Develop an inlet-based floodplain DEM (Digital Elevation Model) for the Pascagoula River. The model employs topography up to the 1.5-meter contour extracted from the Southern Louisiana Gulf Coast Mesh (SL15 Mesh) developed by the Federal Emergency Management Agency (FEMA). 2) Incorporate the inlet-based floodplain model into the Western North Atlantic Tidal (WNAT) model domain, which consists of the Gulf of Mexico, the Caribbean Sea, and the entire portion of the North Atlantic Ocean found west of the 60 degree West meridian, in order to more fully account for the storm surge dynamics occurring within the Pascagoula River. This large-scale modeling approach will utilize high-resolution wind and pressure fields associated with Hurricane Katrina, so that storm surge hydrographs (elevation variance) at the open-ocean boundary locations associated with the localized domain can be adequately obtained. 3) Understand the importance of the various meteorological forcings that are attributable to the storm surge dynamics that are setup within the Pascagoula River. Different implementations of the two model domains (large-scale, including the WNAT model domain; localized, with its focus concentrated solely on the Pascagoula River) will involve the application of tides, storm surge hydrographs and meteorological forcing (winds and pressures) in isolation (i.e., as the single forcing mechanism) and collectively (i.e., together in combination). The following conclusions are drawn from the research presented in this thesis: 1) Incorporating the marsh areas into the preliminary in-bank mesh provides for significant improvement in the astronomic tide simulation; 2) the large-scale modeling approach (i.e., the localized floodplain mesh incorporated into the WNAT model domain) is shown to be most adequate towards simulating storm surge dynamics within the Pascagoula River. Further, we demonstrate the utility of the large-scale model domain towards providing storm surge hydrographs for the open-ocean boundary of the localized domain. Only when the localized domain is forced with the storm surge hydrograph (generated by the large-scale model domain) does it most adequately capture the full behavior of the storm surge. Finally, we discover that while the floodplain description up to the 1.5-m contour greatly improves the model response by allowing for the overtopping of the river banks, a true recreation of the water levels caused by Hurricane Katrina will require a floodplain description up to the 5-m contour.

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This thesis presents a method, Ensemble Kalman Filter (EnKF), applied to a highresolution, shallow water equations model (DG ADCIRC-2DDI) of the Lower St. Johns River with observation data at four gauging stations. EnKF, a sequential data assimilation method for non-linear problems, is developed for tidal flow simulation for estimation of state variables, i.e., water levels and depth-integrated currents for overland unstructured finite element meshes. The shallow water equations model is combined with observation data, which provides the basis of the EnKF applications. In this thesis, EnKF is incorporated into DG ADCIRC-2DDI code to estimate the state variables. Upon its development, DG ADCIRC-2DDI with EnKF is first validated by implementing to a low-resolution, shallow water equations model of a quarter annular harbor with synthetic observation data at six gauging stations. Second, DG ADCIRC-2DDI with EnKF is implemented to a high-resolution, shallow water equations model of the Lower St. Johns River with real observation data at four gauging stations. Third, four different experiments are performed by applying DG ADCIRC-2DDI with EnKF to the Lower St. Johns River.

This dissertation investigates the use of remotely sensed data in coastal tide and inundation models, specifically how these data could be more effectively integrated into model construction and performance assessment techniques. It includes a review of numerical wetting and drying algorithms, a method for constructing a seamless digital terrain model including the handling of tidal datums, an investigation into the accuracy of land use / land cover (LULC) based surface roughness parameterization schemes, an application of a cutting edge remotely sensed inundation detection method to assess the performance of a tidal model, and a preliminary investigation into using 3-dimensional airborne laser scanning data to parameterize surface roughness. A thorough academic review of wetting and drying algorithms employed by contemporary numerical tidal models was conducted. Since nearly all population centers and valuable property are located in the overland regions of the model domain, the coastal models must adequately describe the inundation physics here. This is accomplished by techniques that generally fall into four categories: Thin film, Element removal, Depth extrapolation, and Negative depth. While nearly all wetting and drying algorithms can be classified as one of the four types, each model is distinct and unique in its actual implementation. The use of spatial elevation data is essential to accurate coastal modeling. Remotely sensed LiDAR is the standard data source for constructing topographic digital terrain models (DTM). Hydrographic soundings provide bathymetric elevation information. These data are combined to form a seamless topobathy surface that is the foundation for distributed coastal models. A three-point inverse distance weighting method was developed in order to account for the spatial variability of bathymetry data referenced to tidal datums. This method was applied to the Tampa Bay region of Florida in order to produce a seamless topobathy DTM. Remotely sensed data also contribute to the parameterization of surface roughness. It is used to develop land use / land cover (LULC) data that is in turn used to specify spatially distributed bottom friction and aerodynamic roughness parameters across the model domain. However, these parameters are continuous variables that are a function of the size, shape and density of the terrain and above-ground obstacles. By using LULC data, much of the variation specific to local areas is generalized due to the categorical nature of the data. This was tested by comparing surface roughness parameters computed based on field measurements to those assigned by LULC data at 24 sites across Florida. Using a t-test to quantify the comparison, it was proven that the parameterizations are significantly different. Taking the field measured parameters as ground truth, it is evident that parameterizing surface roughness based on LULC data is deficient. In addition to providing input parameters, remotely sensed data can also be used to assess the performance of coastal models. Traditional methods of model performance testing include harmonic resynthesis of tidal constituents, water level time series analysis, and comparison to measured high water marks. A new performance assessment that measures a model's ability to predict the extent of inundation was applied to a northern Gulf of Mexico tidal model. The new method, termed the synergetic method, is based on detecting inundation area at specific points in time using satellite imagery. This detected inundation area is compared to that predicted by a time-synchronized tidal model to assess the performance of model in this respect. It was shown that the synergetic method produces performance metrics that corroborate the results of traditional methods and is useful in assessing the performance of tidal and storm surge models. It was also shown that the subject tidal model is capable of correctly classifying pixels as wet or dry on over 85% of the sample areas. Lastly, since it has been shown that parameterizing surface roughness using LULC data is deficient, progress toward a new parameterization scheme based on 3-dimensional LiDAR point cloud data is presented. By computing statistics for the entire point cloud along with the implementation of moving window and polynomial fit approaches, empirical relationships were determined that allow the point cloud to estimate surface roughness parameters. A multi-variate regression approach was chosen to investigate the relationship(s) between the predictor variables (LiDAR statistics) and the response variables (surface roughness parameters). It was shown that the empirical fit is weak when comparing the surface roughness parameters to the LiDAR data. The fit was improved by comparing the LiDAR to the more directly measured source terms of the equations used to compute the surface roughness parameters. Future work will involve using these empirical relationships to parameterize a model in the northern Gulf of Mexico and comparing the hydrodynamic results to those of the same model parameterized using contemporary methods. In conclusion, through the work presented herein, it was demonstrated that incorporating remotely sensed data into coastal models provides many benefits including more accurate topobathy descriptions, the potential to provide more accurate surface roughness parameterizations, and more insightful performance assessments. All of these conclusions were achieved using data that is readily available to the scientific community and, with the exception of the Synthetic Aperture Radar (SAR) from the Radarsat-1 project used in the inundation detection method, are available free of charge. Airborne LiDAR data are extremely rich sources of information about the terrain that can be exploited in the context of coastal modeling. The data can be used to construct digital terrain models (DTMs), assist in the analysis of satellite remote sensing data, and describe the roughness of the landscape thereby maximizing the cost effectiveness of the data acquisition.