Development of a Coastal Margin Observation and Assessment System (CMOAS) to capture the episodic events in a shallow bay

Abstract

Corpus Christi Bay (TX, USA) is a shallow wind-driven bay which is designated\nas a National Estuary due to its impact on the economy. But this bay experiences\nperiodic hypoxia (dissolved oxygen <2 mg/l) which threatens aerobic aquatic organisms.\nDevelopment of the Coastal Margin Observation and Assessment System (CMOAS)\nthrough integration of real-time observations with numerical modeling helps to\nunderstand the processes causing hypoxia in this energetic bay. CMOAS also serves as a\ntemplate for the implementation of observational systems in other dynamic ecosystems\nfor characterizing and predicting other episodic events such as harmful algal blooms,\naccidental oil spills, sediment resuspension events, etc.\nState-of-the-art sensor technologies are involved in real-time monitoring of\nhydrodynamic, meteorological and water quality parameters in the bay. Three different\nplatform types used for the installation of sensor systems are: 1) Fixed Robotic, 2)\nMobile, and 3) Remote. An automated profiler system, installed on the fixed robotic\nplatform, vertically moves a suite of in-situ sensors within the water column for continuous measurements. An Integrated Data Acquisition, Communication and Control\nsystem has been configured on our mobile platform (research vessel) for the\nsynchronized measurements and real-time visualization of hydrodynamic and water\nquality parameters at greater spatial resolution. In addition, a high frequency (HF) radar\nsystem has been installed on remote platforms to generate surface current maps for\nCorpus Christi (CC) Bay and its offshore area. This data is made available to\nstakeholders in real-time through the development of cyberinfrastructure which includes\nestablishment of communication network, software development, web services, database\ndevelopment, etc. Real-time availability of measured datasets assists in implementing an\nintegrated sampling scheme for our monitoring systems installed at different platforms.\nWith our integrated system, we were able to capture evidence of an hypoxic event in\nSummer 2007.\nData collected from our monitoring systems are used to drive and validate\nnumerical models developed in this study. The analysis of observational datasets and\ndeveloped 2-D hydrodynamic model output suggests that a depth-integrated model is not\nable to capture the water current structure of CC Bay. Also, the development of a threedimensional\nmechanistic dissolved oxygen model and a particle aggregation transport\nmodel (PAT) helps to clarify the critical processes causing hypoxia in the bay. The\nvarious numerical models and monitoring systems developed in this study can serve as\nvaluable tools for the understanding and prediction of various episodic events dominant\nin other dynamic ecosystems.