Using probability distribution function as a scaling approach to incorporate soil heterogeneity into biogeochemical models for greenhouse gas predictions

Last updated on Aug 8, 2022

Co-PI(s): Jianqiu Zheng, Pacific Northwest National Laboratory and Eric A Davidson, University of Maryland Center for Environmental Science Appalachian Laboratory

Collaborator: Patrick Megonigal, Smithsonian Environmental Research Center and Michael Weintraub, University of Toledo

Total Funding: $300,000

Brief description of the project:
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 pilot-scale and ecosystem-scale functions will control the long-term dynamics of GHGs in these dynamic interfaces. These complex interconnected processes across TAIs are underrepresented in current ecosystem and Earth system models 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 are incorporating probability distributions of redox processes at soil microsites using a coupled modeling-experimental (ModEx) approach.

We are developing 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) using Bayesian MCMC approaches.

We hope that representing redox heterogeneity using probability density (or distribution) functions of soil microsites across TAIs will ultimately contribute to improving Earth System Predictability.