Browsing by Subject "Numerical Model"
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Item DEVELOPMENT OF A COASTAL MARGIN OBSERVATION AND ASSESSMENT SYSTEM (CMOAS) TO CAPTURE THE EPISODIC EVENTS IN A SHALLOW BAY(2010-07-14) Islam, Mohammad S.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.Item Statistical and Realistic Numerical Model Investigations of Anthropogenic and Climatic Factors that Influence Hypoxic Area Variability in the Gulf of Mexico(2012-07-16) Feng, YangThe hypoxic area in the Gulf of Mexico is the second largest in the world, which has received extensive scientific study and management interest. Previous modeling studies have concluded that the increased hypoxic area in the Gulf of Mexico was caused by the increased anthropogenic nitrogen loading of the Mississippi River; however, the nitrogen-area relationship is complicated by many other factors, such as wind, river discharge, and the ratio of Mississippi to Atchafalaya River flow. These factors are related to large-scale climate variability, and thus will not be affected by regional nitrogen reduction efforts. In the research presented here, both statistical (regression) and numerical models are used to study the influence of anthropogenic and climate factors on the hypoxic area variability in the Gulf of Mexico. The numerical model is a three-dimensional, coupled hydrological-biogeochemical model (ROMS-Fennel). Results include: (1) the west wind duration during the summer explain 55% of the hypoxic area variability since 1993. Combined wind duration and nitrogen loading explain over 70% of the variability, and combined wind duration and river discharge explain over 85% of the variability. (2) The numerical model captures the temporal variability, but overestimates the bottom oxygen concentrations. The model shows that the simulated hypoxic area is in agreement with the observations from the year 1991, as long as hypoxia is defined as oxygen concentrations below 3 mg/L rather than below 2 mg/L. (3) The first three modes from an Empirical Orthogonal Function (EOF) analysis of the numerical model output results explain 62%, 8.1% and 4.9% of the variability of the hypoxic area. The Principle Component time series is cross-correlated with wind, dissolved inorganic nitrogen concentration and river discharge. (4) Scenario experiments with the same nitrogen loading, but different duration of upwelling favorable wind, indicate that the upwelling favorable wind is important for hypoxic area development. However, a long duration of upwelling wind decreases the area. (5) Scenario experiments with the same nitrogen loading, but different discharges, indicate that increasing river discharge by 50% increases the area by 42%. Additionally, scenario experiments with the same river discharge, but different nitrogen concentrations, indicate that reducing the nitrogen concentration by 50% decreases the area by 75%. (6) Scenario experiments with the same nitrogen loading, but different flow diver- sions, indicate that if the Atchafalaya River discharges increased to 66.7%, the total hypoxic area increases the hypoxic area by 30%, and most of the hypoxic area moved from east to west Louisiana shelf. Additionally, if the Atchafalaya River discharge decreased to zero, the total hypoxic area increases by 13%. (7) Scenario experiments with the same nitrogen loading, but different nitrogen forms, indicate that if all the nitrogen was in the inorganic forms, the hypoxic area increases by 15%. These results have multiple implications for understanding the mechanisms that control the oxygen dynamics, reevaluating management strategies, and improving the observational methods.