Identifying Data Needed to Reduce Parameter Uncertainty in a Coupled Microbial Soil C and N Decomposition Model

Abstract

Advancements in microbially explicit ecosystem models incorporate increasingly accurate representations of microbial physiology and enzyme‐mediated depolymerization of soil organic matter in predicting biogeochemical responses to global change. However, a major challenge with model structural improvements is the requirement for additional parameters, which are often poorly constrained sources of uncertainty. Furthermore, it is often unclear how to best focus data collection efforts toward reducing model uncertainty. Here, we use Dual Arrhenius Michaelis‐Menten Microbial Carbon and Nitrogen Physiology, a microbially mediated, coupled soil C and N cycling model, as a tool to explore the influence of microbial physiological and enzyme kinetic parameters on model estimates. We first quantify the potential for constraining model parameters using empirical measurements of soil respiration. We then use simulated data to identify which additional sources of data collection from the field would provide the greatest impact for constraining model estimates. We find that modeled soil C and N pools and fluxes are disproportionately sensitive to only a few parameters (e.g., activation energies and microbial CUE), while others exert less influence (e.g., Michaelis‐Menten half‐saturation constants). While some parameters can be constrained by the available data on heterotrophic respiration, the collection of additional data on dissolved organic C and N pools in the soil is identified as a high‐priority data need. Improving our ability to model the interactions of soil microbial physiology, soil chemistry, enzyme activities, and environmental factors on C and N cycling will require closely considering model uncertainties and prioritizing future data collection opportunities based on their impact on model performance.

A framework for variational inference and data assimilation of soil biogeochemical models using state space approximations and normalizing flows

“…In an effort to account for uncertainty in θ values and data observations and encode expert domain beliefs, other comparisons have involved the use of Bayesian Markov chain Monte Carlo (MCMC) inference methods and goodness-of-fit metrics with some success J. Li et al, 2019;Xie et al, 2020;Saifuddin et al, 2021;Wang et al, 2022).…”

A framework for variational inference and data assimilation of soil biogeochemical models using state space approximations and normalizing flows

“…The state variables of SBMs typically are densities or masses of elements in those pools (Manzoni & Porporato, 2009), and heterotrophic soil CO 2 emissions can be estimated from those state values and microbial parameters (Allison et al, 2010). As soil microbe communities influencing organic mass transfer dynamics evolve and shift under the selection pressures of terrestrial warming, SBMs have become an important tool for soil scientists and biogeochemists to quantify changes in soil system activity and predict future heterotrophic soil respiration levels (Sulman et al, 2018;Saifuddin et al, 2021).…”

“…In an effort to account for uncertainty in θ values and data observations and encode expert domain beliefs, other comparisons have involved the use of Bayesian Markov chain Monte Carlo (MCMC) inference methods and goodness-of-fit metrics with some success J. Li et al, 2019;Xie et al, 2020;Saifuddin et al, 2021;Wang et al, 2022).…”

A framework for variational inference and data assimilation of soil biogeochemical models using state space approximations and normalizing flows

“…In an effort to account for uncertainty in θ values and data observations and encode expert domain beliefs, other comparisons have involved the use of Bayesian Markov chain Monte Carlo (MCMC) inference methods and goodness-of-fit metrics with some success J. Li et al, 2019;Xie et al, 2020;Saifuddin et al, 2021;Wang et al, 2022).…”