Coupling X-ray Computed Tomography images with ModEx inspired laboratory manipulation experiment for predicting greenhouse gas fluxes from Terrestrial-Aquatic Interfaces
EMSL Project ID
60398
Abstract
The Terrestrial Aquatic Interfaces (TAIs) with dynamic hydrological exchange represent the most biogeochemically active and diverse systems. Frequent hydrological oscillations due to tidal inundations and storm surges regulate oxidation-reduction(redox)-driven biogeochemical transformations and fluxes of carbon and nutrients across TAIs. Soil microsites, the most biogeochemically active soil components, further complicate such hydrological dynamic-driven redox biogeochemistry by creating spatial heterogeneity and variations in reaction kinetics. The functional forms of the interactions among water, carbon and redox sensitive compounds may differ at microsite-, plot- and ecosystem-scales. How microsite-scale processes manifest into plot-scale and ecosystem-scale functions will control long-term dynamics of GHGs in these dynamic interfaces.
We will leverage EMSL capabilities to evaluate how heterogeneity of redox at soil microsites influence greenhouse gas (GHG) emissions from TAIs using a ModEx (Modeling-Experimental)-inspired laboratory manipulation experiment. We will experimentally impose hydrological fluctuations in the laboratory using intact cores to mimic tidal events and water-table fluctuations in the field and track the associated shifts in microsite PDFs of soil moisture, terminal electron acceptors, and analyze the temporal evolution of soil structural heterogeneity (i.e., pore-network architecture) using X-ray Computed Tomography (CT) images.
We hypothesize that (1) data generated using X-ray CT scans could provide quantitative information on microsite PDFs and (2) Plot-scale and ecosystem-scale dynamics of GHGs can be captured by integrating and upscaling microbial metabolic activities at microsite-scale. Data generated via EMSL capabilities will be used to constrain a redox-informed modeling framework integrated with microsite probability distribution functions (PDFs). The ModEx inspired laboratory results will be used to constrain a new modeling framework we are building by merging the capabilities of microsite PDF (Probability Distribution Function) functions of the DAMM-GHG (Dual Arrhenius and Michaelis Menten-GreenHouse Gas, Sihi et al., 2020, doi: 10.1111/gcb.14855 and Sihi et al., 2021, doi: 10.5194/bg-18-1769-2021) model with a redox reaction network model (Zheng et al., 2019). We will constrain the shape of the microsite PDFs of redox processes using X-ray CT scan data (for diffusions of solutes and gases) coupled with porewater chemistry (for concentrations of terminal electron acceptors) data generated from the laboratory manipulation experiment. Particularly, obtained X-ray CT scans of intact cores will determine soil pore network, which we will use to parameterize inter- and intra-aggregate diffusivity of gas and solutes and associated dynamics of GHGs (carbon dioxide, CO2; methane, CH4; and nitrous oxide, N2O) under fluctuating hydrology. Outcome of this project will evaluate these complex interconnected processes across TAIs, which are underrepresented in current ecosystem and Earth system models. Ultimately, our dynamic modeling framework will (1) capture the heterogeneity of soil microsites driving non-normal distribution of microbial activities and (2) integrate interconnected processes across scales.
The proposed exploratory work closely aligns with the COMPASS program. Soil heterogeneity represents an important yet unresolved component in biogeochemical models. This project will generate fine-scale data to validate a microsite PDF function based computational tool that will greatly advance transferrable modeling capability from fine-scale processes to ecosystem-scale functions, directly supporting BER priorities of understanding multi-scale Earth system dynamics and processes.
Project Details
Project type
Large-Scale EMSL Research
Start Date
2022-10-01
End Date
N/A
Status
Active
Released Data Link
Team
Principal Investigator
Co-Investigator(s)
Team Members