Abstract
Abstract. Soil organic matter (SOM) dynamics in ecosystem-scale biogeochemical models have traditionally been simulated as immeasurable fluxes between conceptually defined pools. This greatly limits how empirical data can be used to improve model performance and reduce the uncertainty associated with their predictions of carbon (C) cycling. Recent advances in our understanding of the biogeochemical processes that govern SOM formation and persistence demand a new mathematical model with a structure built around key mechanisms and biogeochemically relevant pools. Here, we present one approach that aims to address this need. Our new model (MEMS v1.0) is developed from the Microbial Efficiency-Matrix Stabilization framework, which emphasizes the importance of linking the chemistry of organic matter inputs with efficiency of microbial processing and ultimately with the soil mineral matrix, when studying SOM formation and stabilization. Building on this framework, MEMS v1.0 is also capable of simulating the concept of C saturation and represents decomposition processes and mechanisms of physico-chemical stabilization to define SOM formation into four primary fractions. After describing the model in detail, we optimize four key parameters identified through a variance-based sensitivity analysis. Optimization employed soil fractionation data from 154 sites with diverse environmental conditions, directly equating mineral-associated organic matter and particulate organic matter fractions with corresponding model pools. Finally, model performance was evaluated using total topsoil (0–20 cm) C data from 8192 forest and grassland sites across Europe. Despite the relative simplicity of the model, it was able to accurately capture general trends in soil C stocks across extensive gradients of temperature, precipitation, annual C inputs and soil texture. The novel approach that MEMS v1.0 takes to simulate SOM dynamics has the potential to improve our forecasts of how soils respond to management and environmental perturbation. Ensuring these forecasts are accurate is key to effectively informing policy that can address the sustainability of ecosystem services and help mitigate climate change.
Highlights
The biogeochemical processes that govern soil organic matter (SOM) formation and persistence impact more than half of the terrestrial carbon (C) cycle and play a key role in climate–C feedbacks (Jones and Falloon, 2009; Arora et al, 2013)
In this study we describe and demonstrate the application of a new mathematical model (MEMS v1.0) that applies three major concepts of SOM dynamics: (1) litter input chemistry-dependent microbial carbon use efficiency (CUE) informing SOM formation (Cotrufo et al, 2013), (2) separate dissolved and physical pathways to SOM formation (Cotrufo et al, 2015), and (3) soil C saturation related to litter input chemistry (Castellano et al, 2015)
Fifty years after simulations are initialized, more than 75 % of the sensitivity in total soil C stock was due to the maximum specific decay rate of light particulate organic matter (POM)
Summary
The biogeochemical processes that govern soil organic matter (SOM) formation and persistence impact more than half of the terrestrial carbon (C) cycle and play a key role in climate–C feedbacks (Jones and Falloon, 2009; Arora et al, 2013). Empirical data are commonly abstracted and transformed before being used to parameterize or evaluate the processes of SOM formation and persistence that the model is intended to simulate (Elliott et al, 1996; Zimmermann et al, 2007) This has resulted in many conventional SOM models (e.g. RothC, Jenkinson and Rayner, 1977, DNDC, Li et al, 1992, EPIC, Williams et al, 1984, and CENTURY, Parton et al, 1987) being structurally similar (i.e. partitioning total SOM into discrete pools based on turnover times determined from radiocarbon experiments; see Stout and O’Brien, 1973, and Jenkinson, 1977) but each taking different approaches to simplify the complex mechanisms that govern SOM dynamics. Simulations of SOM can vary greatly between models, often predicting contrasting responses to the same driving inputs and environmental change (e.g. Smith et al, 1997)
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