Representing Complex Consumer-Substrate Networks in Soil Biogeochemistry: Development for ESM Land Models with an Application to Litter Decomposition
Many terrestrial biogeochemical problems relevant to climate and climate change can be formulated as consumer-substrate networks, including soil organic matter (SOM) substrate-microbe networks, predator-prey systems, and organic-mineral interactions. However, current land models integrated in Earth System Models (ESMs), like CLM, lack a mechanistic approach to represent the nonlinear consumer-substrate relationships in SOM networks. Rather, existing approaches approximate these nonlinear relationships linearly, and models using these approaches often have to use ad hoc parameterizations in order to match observations (e.g., lignin decomposition dynamics, isotopic fractionation in enzymatic processes). When parameters inferred from the calibration data are applied to other systems, the model predictions are usually very uncertain.
We addressed this gap by putting the consumer-substrate network in the context of well-studied equilibrium chemistry networks. By using the total quasi-steady state assumption and perturbation theory, we obtained a new representation of these kinetics that we termed the equilibrium chemistry approximation (ECA).We applied ECA to the litter decomposition problem and found that the approach consistently represented the nonlinear competition between consumers for a range of substrates. While being more parsimonious than similar microbial decomposition models, our model explained, for the first time, lignin decomposition dynamics and co-evolution of microbes and litter chemistry consistent with observations. We are currently incorporating ECA into CESM/CLM4.5 to improve the representation of nitrogen dynamics and enable a more mechanistic representation of litter and soil organic matter decomposition dynamics. Because the concepts and model were developed in a general consumer-substrate construct, our new model will also be applicable to problems from other fields, such as predator-prey systems.
This research was supported by the Director, Office of Science, Office of Biological and Environmental Research of the US Department of Energy, under contract no. DE-AC02- 05CH11231, as part of their Regional and Global Climate Modeling Program; and by the Next-Generation Ecosystem Experiments (NGEE Arctic) project, supported by the Office of Biological and Environmental Research in the DOE Office of Science under contract no. DE-AC02-05CH11231.