Corpus Christi Bay (TX, USA) is a shallow wind-driven bay which is designated
as a National Estuary due to its impact on the economy. But this bay experiences
periodic hypoxia (dissolved oxygen <2 mg/l) which threatens aerobic aquatic organisms.
Development of the Coastal Margin Observation and Assessment System (CMOAS)
through integration of real-time observations with numerical modeling helps to
understand the processes causing hypoxia in this energetic bay. CMOAS also serves as a
template for the implementation of observational systems in other dynamic ecosystems
for characterizing and predicting other episodic events such as harmful algal blooms,
accidental oil spills, sediment resuspension events, etc.
State-of-the-art sensor technologies are involved in real-time monitoring of
hydrodynamic, meteorological and water quality parameters in the bay. Three different
platform types used for the installation of sensor systems are: 1) Fixed Robotic, 2)
Mobile, and 3) Remote. An automated profiler system, installed on the fixed robotic
platform, vertically moves a suite of in-situ sensors within the water column for continuous measurements. An Integrated Data Acquisition, Communication and Control
system has been configured on our mobile platform (research vessel) for the
synchronized measurements and real-time visualization of hydrodynamic and water
quality parameters at greater spatial resolution. In addition, a high frequency (HF) radar
system has been installed on remote platforms to generate surface current maps for
Corpus Christi (CC) Bay and its offshore area. This data is made available to
stakeholders in real-time through the development of cyberinfrastructure which includes
establishment of communication network, software development, web services, database
development, etc. Real-time availability of measured datasets assists in implementing an
integrated sampling scheme for our monitoring systems installed at different platforms.
With our integrated system, we were able to capture evidence of an hypoxic event in
Summer 2007.
Data collected from our monitoring systems are used to drive and validate
numerical models developed in this study. The analysis of observational datasets and
developed 2-D hydrodynamic model output suggests that a depth-integrated model is not
able to capture the water current structure of CC Bay. Also, the development of a threedimensional
mechanistic dissolved oxygen model and a particle aggregation transport
model (PAT) helps to clarify the critical processes causing hypoxia in the bay. The
various numerical models and monitoring systems developed in this study can serve as
valuable tools for the understanding and prediction of various episodic events dominant
in other dynamic ecosystems.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-05-501 |
| Date | 2009 May 1900 |
| Creators | Islam, Mohammad S. |
| Contributors | Bonner, James S. |
| Source Sets | Texas A and M University |
| Language | English |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | application/pdf |
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