The first part of this thesis deals with studying the effect of the specified bathymetric resolution and ideal bathymetric form parameters on the output from the wave and hydrodynamic modules of Delft-3D. This thesis then describes the use of an optimization to effectively reduce the required bathymetric sampling for input to a numerical forecast model, by using the model’s sensitivity to this input. A genetic algorithm is developed to gradually evolve the survey path for a ship, AUV, or other measurement platform to an optimum, with the resulting effect of the corresponding measured bathymetry on the model, used as a metric. Starting from an initial simulated set of possible random or heuristic sampling paths over the given bathymetry using certain constraints like limited length of track, the algorithm can be used to arrive at the path that would provide the best possible input to the model under those constraints. This suitability is tested by a comparison of the model results obtained by using these new simulated observations, with the results obtained using the best available bathymetry. Two test study areas were considered, and the algorithm was found to consistently converge to a sampling pattern that best captured the bathymetric variability critical to the model prediction.

The first part of this thesis deals with studying the effect of the specified bathymetric resolution and ideal bathymetric form parameters on the output from the wave and hydrodynamic modules of Delft-3D. This thesis then describes the use of an optimization to effectively reduce the required bathymetric sampling for input to a numerical forecast model, by using the model’s sensitivity to this input. A genetic algorithm is developed to gradually evolve the survey path for a ship, AUV, or other measurement platform to an optimum, with the resulting effect of the corresponding measured bathymetry on the model, used as a metric. Starting from an initial simulated set of possible random or heuristic sampling paths over the given bathymetry using certain constraints like limited length of track, the algorithm can be used to arrive at the path that would provide the best possible input to the model under those constraints. This suitability is tested by a comparison of the model results obtained by using these new simulated observations, with the results obtained using the best available bathymetry. Two test study areas were considered, and the algorithm was found to consistently converge to a sampling pattern that best captured the bathymetric variability critical to the model prediction.

Approved for public release, distribution is unlimited / Accurate predictions of nearshore wave conditions are critical to the success of military operations in the littoral environment. Although linear spectral-refraction theory is used by the main operational forecasting centers in the world for these predictions, owing to a lack of field studies its accuracy in regions of complex bathymetry such as steep shoals and submarine canyons is unknown. This study examines the accuracy of linear spectral-refraction theory in areas of complex nearshore bathymetry with three months of extensive wave data collected during the Nearshore Canyon Experiment (NCEX) held in the fall of 2003. The field site, off La Jolla California, is characterized by two submarine canyons that strongly affect the propagation of long period Pacific swell. Data from 7 directional waverider buoys, 17 bottom pressure recorders, and 12 pressure-velocity sensors, were examined and compared to predictions made by a high resolution spectral-refraction model. Analysis reveals large spatial variation in wave heights over the area especially in the vicinity of the canyon heads, where wave heights vary by as much as an order of magnitude over a few hundred meters. This extreme variation in wave conditions across the canyons is surprisingly well described by refraction theory with typical errors of nearshore wave height predictions of about 20 percent. / Lieutenant, Royal Australian Navy