Uncertainty in the fate of soil organic carbon: A comparison of three conceptually different decomposition models at a larch plantation

Abstract

Conventional Q10 soil organic matter decomposition models and more complex microbial models are available for making projections of future soil carbon dynamics. However, it is unclear (1) how well the conceptually different approaches can simulate observed decomposition and (2) to what extent the trajectories of long-term simulations differ when using the different approaches. In this study, we compared three structurally different soil carbon (C) decomposition models (one Q10 and two microbial models of different complexity), each with a one-and two-horizon version. The models were calibrated and validated using 4 years of measurements of heterotrophic soil CO 2 efflux from trenched plots in a Dahurian larch (Larix gmelinii Rupr.) plantation. All models reproduced the observed heterotrophic component of soil CO 2 efflux, but the trajectories of soil carbon dynamics differed substantially in 100 year simulations with and without warming and increased litterfall input, with microbial models that produced better agreement with observed changes in soil organic C in long-term warming experiments. Our results also suggest that both constant and varying carbon use efficiency are plausible when modeling future decomposition dynamics and that the use of a short-term (e.g., a few years) period of measurement is insufficient to adequately constrain model parameters that represent long-term responses of microbial thermal adaption. These results highlight the need to reframe the representation of decomposition models and to constrain parameters with long-term observations and multiple data streams. We urge caution in interpreting future soil carbon responses derived from existing decomposition models because both conceptual and parameter uncertainties are substantial.

Incorporating microbial dormancy dynamics into soil decomposition models to improve quantification of soil carbon dynamics of northern temperate forests

“…Our modeling results demonstrate that the ecosystem‐level controls on dormancy at large spatial scales are different from that at local transient scales. This suggests that both site‐level and spatial data should be used for model validation, because it is usually easier for models to reproduce site‐level, short‐term observations with data assimilation techniques, but much more difficult to capture spatial patterns [ Todd‐Brown et al ., ] and long‐term dynamics [ He et al ., ]. In this study, we successfully reproduced soil R H at six temperature forest sites, but our extrapolated soil R H revealed the potential issues with applying Michaelis‐Menten kinetics on ecosystem scales and yielded high soil R H in the northeastern U.S. due to the high SOC content in that region.…”

How Do Tilia cordata Greenspire Trees Cope with Drought Stress Regarding Their Biomass Allocation and Ecosystem Services?

“…Ecological process-based models have been applied widely for ecological issues, including biodiversity, phenology, hydrology and ecosystem services [54]. To obtain reliable predictions, however, the process-based models need to be validated first, in combination with observational data [55].…”

Modelling the continuous exchange of carbon between living organisms, the soil and the atmosphere

“…We believe that this is due to the failure to take account of the functional role of microorganisms in many models which focus mainly on total C stocks, rather than on transfers between the microbial biomass (MB) and metabolites with varying stabilities. Although some emerging models take account of microbial activity (Allison et al 2010;Pansu et al 2004;Schimel and Weintraub 2003), a new generation is required to reframe the equations on the main mechanisms of direct microbial control over decomposition (He et al 2014;Zhou et al 2011;Ibrahim et al 2013) without an excessive mathematical complexity (ToddBrown et al 2012) and better quantify the incidence of C inputs on the MB (Xu et al 2013) and the stability of ecological systems (Moore et al 2004).…”