Doctor of Philosophy (PhD)
First Advisor's Name
Omar I. Abdul-Aziz
First Advisor's Committee Title
Second Advisor's Name
Leonard J. Scinto
Second Advisor's Committee Title
Third Advisor's Name
Third Advisor's Committee Title
Fourth Advisor's Name
Hector R. Fuentes
Fourth Advisor's Committee Title
Fifth Advisor's Name
Arindam Gan Chowdhury
Fifth Advisor's Committee Title
Ecological Engineering, Greenhouse Gas, Data-analytics, Forest Ecosystems, Wetlands, Scaling, Similitude, Modeling, Robust Predictions
Date of Defense
The land-atmospheric exchanges of carbon dioxide (CO2) and methane (CH4) are major drivers of global warming and climatic changes. The greenhouse gas (GHG) fluxes indicate the dynamics and potential storage of carbon in terrestrial and wetland ecosystems. Appropriate modeling and prediction tools can provide a quantitative understanding and valuable insights into the ecosystem carbon dynamics, while aiding the development of engineering and management strategies to limit emissions of GHGs and enhance carbon sequestration. This dissertation focuses on the development of data-analytics tools and engineering models by employing a range of empirical and semi-mechanistic approaches to robustly predict ecosystem GHG fluxes at variable scales.
Scaling-based empirical models were developed by using an extended stochastic harmonic analysis algorithm to achieve spatiotemporally robust predictions of the diurnal cycles of net ecosystem exchange (NEE). A single set of model parameters representing different days/sites successfully estimated the diurnal NEE cycles for various ecosystems. A systematic data-analytics framework was then developed to determine the mechanistic, relative linkages of various climatic and environmental drivers with the GHG fluxes. The analytics, involving big data for diverse ecosystems of the AmeriFLUX network, revealed robust latent patterns: a strong control of radiation-energy variables, a moderate control of temperature-hydrology variables, and a relatively weak control of aerodynamic variables on the terrestrial CO2 fluxes.
The data-analytics framework was then employed to determine the relative controls of different climatic, biogeochemical and ecological drivers on CO2 and CH4 fluxes from coastal wetlands. The knowledge was leveraged to develop nonlinear, predictive models of GHG fluxes using a small set of environmental variables. The models were presented in an Excel spreadsheet as an ecological engineering tool to estimate and predict the net ecosystem carbon balance of the wetland ecosystems. The research also investigated the emergent biogeochemical-ecological similitude and scaling laws of wetland GHG fluxes by employing dimensional analysis from fluid mechanics. Two environmental regimes were found to govern the wetland GHG fluxes. The discovered similitude and scaling laws can guide the development of data-based mechanistic models to robustly predict wetland GHG fluxes under a changing climate and environment.
Ishtiaq, Khandker S., "Robust Modeling and Predictions of Greenhouse Gas Fluxes from Forest and Wetland Ecosystems" (2015). FIU Electronic Theses and Dissertations. 2287.
Bioinformatics Commons, Civil Engineering Commons, Climate Commons, Forest Sciences Commons
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