Nowcast/forecast of storm surge and drawdown using a hybrid FE/ANN approach Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/fq977x45x

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  • In the northwestern Gulf of Mexico there is a need for reliable water level forecasts to facilitate safe commercial navigation, marine construction, and emergency management. Though the low amplitude tides of the region can be predicted with conventional harmonic techniques, frequent strong storms make accurate forecasts of water levels difficult. Prediction of surge associated with storms is useful for management of activities in low lying coastal areas subject to flooding, while prediction of drawdown is important for estimating the under keel clearance of deep draft vessels. First, this thesis presents the analysis of nine years of observations to quantify the effects of extra-tropical storms on water level fluctuations at four locations along the northwestern coast of the Gulf of Mexico. The monthly root-mean-square of the meteorological component of the observed water level is shown to be of similar magnitude to the astronomical tide from September to April, attributed to the weekly passage of storm systems. Return periods for extreme maximum (surge) and minimum (drawdown) meteorological water levels associated with these storms are computed using a 9-year record of observed water levels. Significant surge and drawdown water levels, defined as those exceeding 30 cm, are shown to have return periods ranging from 0.17 to 7.14 years at the four stations studied. At the entrance to Galveston Bay, Texas, both significant storm surge and drawdown can be expected to occur about four times each year. These results reinforced the need to include meteorological forcing in the forecasting of water levels in the region. In an effort to provide more accurate water level forecasting within Galveston Bay during storms, the finite element (FE) hydrodynamic model ADCIRC was applied to simulate a range of observed storm events by inclusion of wind stress and remote forcing from the Gulf of Mexico. Comparisons with observations showed the limited accuracy of ADCIRC, as applied here, in predicting the water level response for drawdown events. Thus, the ADCIRC output was incorporated into a data assimilation method utilizing artificial neural networks (ANN). This hybrid FE/ANN approach substantially improved the pure FE simulations for short term forecasts and also outperformed other forecasting methods. With further refinement, this hybrid approach could provide operational forecasts of water levels within Galveston Bay during storms.
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