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

Mustafa Saifuddin , Rose Z. Abramoff (1) , Eric A. Davidson , Michael C. Dietze , Adrien C. Finzi

1 LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette]

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.

Keywords: Bayesian parameter estimation; soil biogeochemical model; soil microbial physiology; soil carbon and nitrogen cycling; heterotrophic respiration

Domains: Sciences of the Universe [physics]

Mustafa Saifuddin, Rose Z. Abramoff, Eric A. Davidson, Michael C. Dietze, Adrien C. Finzi. Identifying Data Needed to Reduce Parameter Uncertainty in a Coupled Microbial Soil C and N Decomposition Model. Journal of Geophysical Research: Biogeosciences, 2021, 126, ⟨10.1029/2021JG006593⟩. ⟨insu-03721905⟩