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Bayesian Modeling of Coastal Eutrophication to Inform Management Solutions for Hypoxia and Algal Blooms.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian Modeling of Coastal Eutrophication to Inform Management Solutions for Hypoxia and Algal Blooms./
作者:	Katin, Alexey.
面頁冊數:	1 online resource (160 pages)
附註:	Source: Dissertations Abstracts International, Volume: 82-10, Section: B.
Contained By:	Dissertations Abstracts International82-10B.
標題:	Eutrophication. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28392961click for full text (PQDT)
ISBN:	9798708717504

Bayesian Modeling of Coastal Eutrophication to Inform Management Solutions for Hypoxia and Algal Blooms.
Katin, Alexey.

Bayesian Modeling of Coastal Eutrophication to Inform Management Solutions for Hypoxia and Algal Blooms. - 1 online resource (160 pages)

Source: Dissertations Abstracts International, Volume: 82-10, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2021.

Includes bibliographical references

Eutrophication or nutrient overenrichment is a common problem in many aquatic systems worldwide, promoting excessive growth of algae (algal blooms) and subsequent hypoxia, defined by dissolved oxygen level below 2 mg/L. Although there is considerable scientific knowledge about the major drivers of algal blooms and hypoxia, developing cost-effective sensible measures for improving aquatic water quality remains challenging. Therefore, identifying the complex interactions between the diverse drivers of eutrophication is important to the formulation of long- and short-term management strategies. This research focuses on improving our understanding of hypoxia and algal dynamics for the Neuse River Estuary (NRE) and the Northern Gulf of Mexico (NGoM). First, a mechanistic model to predict dissolved oxygen is developed, accounting for the complex interactions of hydrometeorologic and anthropogenic influences on different time scales for the NRE. Second, statistical and mechanistic predictive models are created to understand how environmental factors control phytoplankton in the NRE and to test the system sensitivity to nutrient loading variations. Third, an existing NGoM hypoxia model is tested for forecasting dissolved oxygen up to four months ahead, considering the importance of various data inputs in forecasting performance. As environmental models typically possess substantial uncertainty, models developed within this research are calibrated to multidecadal datasets using a Bayesian framework, which accommodates previous knowledge about parameters and allows for obtaining probabilistic predictions. Overall, this work suggests that nutrient loading reductions are effective in decreasing both the number of hypoxic days and the mean chlorophyll a concentration in the NRE. Also, NGoM forecasting test results indicate that inclusion of spring meteorological data and projected summer riverine inputs can help constrain forecast uncertainty while explaining about half of the variability in the observed hypoxic area.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798708717504Subjects--Topical Terms:

896350
Eutrophication.
Subjects--Index Terms:

EutrophicationIndex Terms--Genre/Form:

542853
Electronic books.

Bayesian Modeling of Coastal Eutrophication to Inform Management Solutions for Hypoxia and Algal Blooms.
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502 $a Thesis (Ph.D.)--North Carolina State University, 2021.
504 $a Includes bibliographical references
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