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Prediction of estuarine morphological evolution

Posted on:2009-06-18Degree:Ph.DType:Dissertation
University:Mississippi State UniversityCandidate:Savant, GauravFull Text:PDF
GTID:1442390005456390Subject:Engineering
Abstract/Summary:
Estuaries are vital environmental and economic resources, providing habitat for thousands of species, absorbing runoff, and supporting recreation and commerce. Yet, despite their importance, estuaries are threatened by human activities. Empirical Orthogonal Function (EOF) analysis and Cross Spectral techniques were used in the analysis and prediction of estuarine morphology. The estuaries selected for study were Suisun Bay, CA and Mobile Bay, AL. It was found that EOF is an effective and efficient technique to analyze morphology.;EOF analysis on Suisun Bay revealed that the bay is depositional particularly in the shallow bays of Honker and Grizzly, whereas the main channels as well as Carquinez Straits maintained their depths throughout the period studied. Utilizing a Cross spectral technique, Amplitude Response Function (ARF), temporal eigenfunctions for the bay were determined for year 2100. The temporal eigenfunctions were predicted for cases where river inflows to the bay were varied by 1 standard deviation unit. These predicted eigenfunction values combined with the eigenvalues resulted in the recovery of predicted depths for year 2100. It was found that Suisun Bay remains depositional through the year 2100. This depositional behavior results in the decrease of bay volume to almost 40% of the volume in 1989.;EOF analysis on Mobile Bay revealed that the bay is predominantly depositional except in the navigation channel and the shoreline of the Bay. The navigation channel maintaining it depth is attributed to the regular dredging carried out to facilitate shipping. The second temporal eigenfunction showed a close correlation with river inflows as in the case of Suisun Bay. However, a cross correlation performed on the second temporal eigenfunction and inflows revealed that the response of the eigenfunction is lagged by almost 9 years. An ARF on the temporal eigenfunctions combined with a reverse EOF resulted in the formation of bathymetric datasets for the year 2100 for inflows variation of 1 standard deviation. It was revealed that increasing the flows results in an increase of bay volume by approximately 30% and a decrease in flows results in a loss of volume by approximately 20%.
Keywords/Search Tags:EOF, Volume
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