Doctor of Philosophy (PhD)


Oceanography and Coastal Sciences

Document Type

Restricted Dissertation


Water quality is examined in two contrasting estuaries, Barataria Bay and Apalachicola Bay, in the northern Gulf of Mexico (NGOM) using in situ and satellite observations. The two estuaries considered in this study are unique - Barataria Bay represents a sediment-starved degrading system while Apalachicola Bay, a naturally intact system, is threatened by a two decade long tri-state water distribution conflict. The present state of water quality parameters, their controlling processes, and distributions are largely limited due to scarcity of synoptic measurements in these estuaries. In this study, a Landsat-based band-ratio algorithm is presented first to examine spatial variability of CDOM abundance during major forcing events in Barataria Bay. The results show that the meteorological and hydrological forcings tend to disrupt general trends of CDOM in spring and winter when the bay experiences elevated Mississippi River discharge and the passages of cold fronts. The idea of using a band-ratio algorithm is extended further to study CDOM dynamics, for the first-time using VIIRS sensor, in Apalachicola Bay. With conservative CDOM-DOC relationships, NCOM-based surface currents, and empirically-derived CDOM maps, DOC export fluxes are estimated which represent ~7% and ~21% of the 110-year mean spring and fall exports for the Mississippi River. A 7-year time-series of turbidity and Landsat imagery are used to demonstrate a simple but robust technique to monitor turbidity in Apalachicola Bay. Seasonal turbidity maps indicate distinct patterns of moderate to high turbid waters in spring and winter, and low to moderately turbid waters in summer and fall. Water quality monitoring strategy presented in this study supports the US EPA’s Clean Water Act to protect human-health and the environment. The empirical relationships presented in the previous studies of this dissertation are limited to area of studies; however, a VIIRS-based tuned quasi-analytical algorithm (QAA-V) is presented to monitor water inherent optical properties (IOPs) in different estuaries in the NGOM. As IOPs are directly associated to the abundance of water constituents, QAA-V can provide a useful tool for regional, state, and federal agencies to monitor water quality for making decisions concerning strategies and plans to mitigate environmental problems.



Document Availability at the Time of Submission

Student has submitted appropriate documentation to restrict access to LSU for 365 days after which the document will be released for worldwide access.

Committee Chair

D'Sa, Eurico