Robust Modeling of Greenhouse Gas (GHG) Fluxes from Coastal Wetland Ecosystems
Wednesday, 17 December 2014: 8:30 AM
Many critical wetland biogeochemical processes are still largely unknown or poorly understood at best. Yet, available models for predicting wetland greenhouse gas (GHG) fluxes (e.g., CO2, CH4, and N2O) are generally mechanistic in nature. This knowledge gap leads to inappropriate process descriptions or over-parameterizations in existing mechanistic models, which often fail to provide accurate and robust predictions across time and space. We developed a systematic data-analytics and informatics method to identify the dominant controls and quantify the relative linkages of wetland GHG fluxes in relation to various hydro-climatic, sea level, biogeochemical and ecological drivers. The method was applied to data collected from 2012-14 through an extensive field campaign from different blue carbon sites of Waquoit Bay, MA. Multivariate pattern recognition techniques of principal component and factor analyses were employed to identify the dominant controls of wetland GHG fluxes; classifying and grouping process variables based on their similarity and interrelation patterns. Power-law based partial least squares regression models were developed to quantify the relative linkages of major GHGs with different process drivers and stressors, as well as to achieve site-specific predictions of GHG fluxes. Wetland biogeochemical similitude and scaling laws were also investigated to unravel emergent patterns and organizing principles of wetland GHG fluxes. The research findings will guide the development of parsimonious empirical to appropriate mechanistic models for spatio-temporally robust predictions of GHGs fluxes and carbon sequestration from coastal wetland ecosystems. The research is part of two current projects funded by the National Oceanic and Atmospheric Administration and the National Science Foundation; focusing on wetland data collections, knowledge formation, formulation of robust GHGs prediction models, and development of ecological engineering tools.