PS 24-44 - Microbial explicit processes improve modeled soil carbon dynamics

Background/Question/Methods

Globally, soils hold approximately half of ecosystem carbon and can serve as a source or sink depending on climate, vegetation, management, and disturbance regimes. Understanding how soil carbon dynamics are influenced by these factors is essential to evaluate proposed natural climate solutions and policy regarding net ecosystem carbon balance. While there is still uncertainty surrounding processes that affect soil carbon dynamics, it is apparent that soil microbes play a key role in both carbon fluxes and stabilization. Over the last two decades, there has been a debate in the literature about the inclusion of microbial-explicit processes in biogeochemical models. Here, we incorporated functions of a stand-alone soil model (FUN-CORPSE) into the DayCent biogeochemical model to better represent microbial-explicit processes of decomposition and soil carbon stabilization. Specifically, the new model has live and dead microbe pools that influence routing of carbon to chemically and physically protected pools, Michaelis-Menten kinetics rather than first-order kinetics in the decomposition function, and feedbacks between decomposition and live microbial pool size. We evaluated the performance of microbial and first-order models using observations of net ecosystem production, ecosystem respiration, soil respiration, microbial biomass, and soil carbon from long-term research sites in North America.

Results/Conclusions

Live microbial biomass pools in the new model were validated with measurements taken at the beginning and middle of the growing season. For both measurement dates, modeled microbial biomass was within the standard error of the observed means. The microbial-explicit model had better model-data agreement for ecosystem respiration (R² = 0.77) compared to the first-order model (R² = 0.69). It also had better seasonal representation of soil carbon fluxes compared to the first-order model which consistently overestimated winter soil respiration. Both models simulated total soil carbon within the observed standard error. However, the microbial model allocated less soil carbon to the passive pool and more to the slow pool than the first-order model. Response to disturbance and management varied between the models. For example, in historic agricultural simulations the microbial model had higher soil carbon loss in response to poor cultivation practices in the era leading up to the Dust Bowl but increased soil carbon at faster rates when agricultural practices improved during the Green Revolution. It’s clear that adding microbial-explicit mechanisms to ecosystem models will affect model predictions of ecosystem carbon balances, particularly when evaluating management decisions, but more research is necessary to validate disturbance response and pool allocation.