Using probability distribution function as a scaling approach to incorporate soil heterogeneity into biogeochemical models for greenhouse gas predictions
| Active Dates | 8/15/2021-8/14/2024 |
|---|---|
| Program Area | Environmental Systems Science |
Project Description
Using probability distribution function as a scaling approach to incorporate soil heterogeneity into biogeochemical models for greenhouse gas predictions
D. Sihi, Emory University (Principal Investigator)
J. Zheng, Pacific Northwest National Laboratory (Co-Investigator)
E. Davidson, University of Maryland Center for Environmental Science, Appalachian Laboratory (Co-Investigator)
P. Megonigal, Smithsonian Environmental Research Center (Unfunded Collaborator)
M. Weintraub, University of Toledo (Unfunded Collaborator)
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. These complex interconnected processes across TAIs are underrepresented in current ecosystem and Earth system models (Bailey et al., 2017)because we lack a dynamic modeling framework that (1) captures the heterogeneity of soil microsites driving non-normal distribution of microbial activities and (2) integrates interconnected processes across scales.
We propose a new modeling framework to capture the heterogeneity of soil microsites to enable dynamic predictions of redox processes and associated GreenHouse Gas (GHG) emissions across the Terrestrial Aquatic Interfaces (TAIs). Our overall goal is to predict GHG dynamics in the TAIs by incorporating probability distributions of redox processes at soil microsites using a coupled modeling-experimental (ModEx) approach. We will build a new modeling framework merging the capabilities of microsite Probability Distribution Functions (PDFs) of the DAMM-GHG (Dual Arrhenius and Michaelis Menten-GreenHouseGas, Sihi et al., 2020a, b) model with a redox reaction network model (Zheng et al., 2019). The new model framework contains three key components: (1) Microsite PDFs, (2) PDF-constrained redox reaction networks, and (3) Redox reaction networks within soil pore-network informing diffusion-limitation of substrates related to productions and consumptions of GHGs. We will test our modeling framework by synthesizing long-term field data, coupling laboratory (and field) manipulation experiments, and leveraging potential activities in the COMPASS program. Specifically, we will focus our study on a tidal wetland site (Global Change Research Wetland, GCREW, in MD) and a freshwater wetland site (Old Woman Creek National Estuarine Research Reserve in OH).
The proposed exploratory work closely aligns with the COMPASS program goal to advance understanding of the interactions of the ecology and biogeochemistry of microbes, water, soils, and plants within coastal TAIs, and to improve representations of coupled TAI processes in site/regional scale models and ultimately Earth system models like E3SM. Soil heterogeneity represents an important yet unresolved component in biogeochemical models. This project, building and validating a microsite PDF function based computational tool, represents a great advance in generating transferrable modeling capabilityfrom fine-scale processes to ecosystem-scale functions (Sihi et al., 2018), directly supporting BER priorities of understanding multi-scale Earth system dynamics and processes. The proposed ModEx work is tightly aligned with DOE-BER missions supporting robust development of models that examine the feedback between biosphere and Earth’s climate system.
D. Sihi, Emory University (Principal Investigator)
J. Zheng, Pacific Northwest National Laboratory (Co-Investigator)
E. Davidson, University of Maryland Center for Environmental Science, Appalachian Laboratory (Co-Investigator)
P. Megonigal, Smithsonian Environmental Research Center (Unfunded Collaborator)
M. Weintraub, University of Toledo (Unfunded Collaborator)
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. These complex interconnected processes across TAIs are underrepresented in current ecosystem and Earth system models (Bailey et al., 2017)because we lack a dynamic modeling framework that (1) captures the heterogeneity of soil microsites driving non-normal distribution of microbial activities and (2) integrates interconnected processes across scales.
We propose a new modeling framework to capture the heterogeneity of soil microsites to enable dynamic predictions of redox processes and associated GreenHouse Gas (GHG) emissions across the Terrestrial Aquatic Interfaces (TAIs). Our overall goal is to predict GHG dynamics in the TAIs by incorporating probability distributions of redox processes at soil microsites using a coupled modeling-experimental (ModEx) approach. We will build a new modeling framework merging the capabilities of microsite Probability Distribution Functions (PDFs) of the DAMM-GHG (Dual Arrhenius and Michaelis Menten-GreenHouseGas, Sihi et al., 2020a, b) model with a redox reaction network model (Zheng et al., 2019). The new model framework contains three key components: (1) Microsite PDFs, (2) PDF-constrained redox reaction networks, and (3) Redox reaction networks within soil pore-network informing diffusion-limitation of substrates related to productions and consumptions of GHGs. We will test our modeling framework by synthesizing long-term field data, coupling laboratory (and field) manipulation experiments, and leveraging potential activities in the COMPASS program. Specifically, we will focus our study on a tidal wetland site (Global Change Research Wetland, GCREW, in MD) and a freshwater wetland site (Old Woman Creek National Estuarine Research Reserve in OH).
The proposed exploratory work closely aligns with the COMPASS program goal to advance understanding of the interactions of the ecology and biogeochemistry of microbes, water, soils, and plants within coastal TAIs, and to improve representations of coupled TAI processes in site/regional scale models and ultimately Earth system models like E3SM. Soil heterogeneity represents an important yet unresolved component in biogeochemical models. This project, building and validating a microsite PDF function based computational tool, represents a great advance in generating transferrable modeling capabilityfrom fine-scale processes to ecosystem-scale functions (Sihi et al., 2018), directly supporting BER priorities of understanding multi-scale Earth system dynamics and processes. The proposed ModEx work is tightly aligned with DOE-BER missions supporting robust development of models that examine the feedback between biosphere and Earth’s climate system.
Award Recipient(s)
- Emory University (PI: Sihi, Debjani)