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Bayesian Spatial Model Development of Soil Core Organic Matter as a proxy for Blue Carbon Stocks within the Chesapeake Bay
Longo, Christian
Longo, Christian
Abstract
Blue carbon is carbon captured and stored within bodies of water and their ecosystems. Blue carbon stocks are very important due to their ability to store carbon away from the atmosphere. The destruction of these stocks can accelerate climate change. In particular, we wish to assess blue carbon stock within the Chesapeake Bay. Previous studies have only used geographical features to predict blue carbon stock levels. The big picture question this thesis was meant to answer is: What is the best approach for building a statistical model that factors in both spatial parameters and geographical features to predict blue carbon stocks across the Chesapeake Bay? A previously acquired data set from 1990 on soil core organic matter can be used as a pilot study for this purpose. This thesis employs a spatially explicit statistical model to predict organic matter in conjunction with geographical features. This quantitative approach had not been widely used for blue carbon stock studies and is shown in this thesis to be a promising approach towards a comprehensive representation of blue carbon stock systems.
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2022-05-01
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Computational & Applied Mathematics & Statistics