Soil moisture is of primary importance for predicting the evolution of soil carbon stocks and fluxes, both because it strongly controls organic matter decomposition and because it is predicted to change at global scales in the following decades. However, the soil functions used to model the heterotrophic respiration response to moisture have limited empirical support and introduce an uncertainty of at least 4% in global soil carbon stock predictions by 2100. The necessity of improving the representation of this relationship in models has been highlighted in recent studies. Here we present a data-driven analysis of soil moisture-respiration relations based on 90 soils. With the use of linear models we show how the relationship between soil heterotrophic respiration and different measures of soil moisture is consistently affected by soil properties. The empirical models derived include main effects and moisture interaction effects of soil texture, organic carbon content and bulk density. When compared to other functions currently used in different soil biogeochemical models, we observe that our results can correct biases and reconcile differences within and between such functions. Ultimately, accurate predictions of the response of soil carbon to future climate scenarios will require the integration of soil-dependent moisture-respiration functions coupled with realistic representations of soil water dynamics.

Subsoils are known to harbor large amounts of soil organic carbon (SOC) and may represent key global carbon (C) sinks given appropriate management. Although rhizodeposition is a major input pathway of organic matter to subsoils, little knowledge exists on C dynamics, particularly stabilization mechanisms, such as soil aggregation, in the rhizosphere of different soil depths. The aim of this study was to investigate the influence of natural and elevated root exudation on C allocation and aggregation in the topsoil and subsoil of a mature European beech (Fagus sylvatica L.) forest. We experimentally added model root exudates to soil at two different concentrations using artificial roots and analyzed how these affect SOC, nitrogen, microbial community composition, and size distribution of water-stable aggregates. Based on the experimental data, a mathematical model was developed to describe the spatial distribution of the formation of soil aggregates and their binding strength. Our results demonstrate that greater exudate additions affect the microbial community composition in favor of fungi which promote the formation of macroaggregates. This effect was most pronounced in the C-poor subsoil, where macroaggregation increased by 86% and SOC content by 10%. Our modeling exercise reproduced the observed increase in subsoil SOC at high exudate additions. We conclude that elevated root exudation has the potential to increase biotic macroaggregation and thus the C sink strength in the rhizosphere of forest subsoils.

Soil is the major terrestrial reservoir of carbon and a substantial part of this carbon is stored in deep layers, typically deeper than 50 cm below the surface. Several studies underlined the quantitative importance of this deep soil organic carbon (SOC) pool and models are needed to better understand this stock and its evolution under climate and land-uses changes. In this study, we tested and compared three simple theoretical models of vertical transport for SOC against SOC profiles measurements from a long-term bare fallow experiment carried out by the Central-Chernozem State Natural Biosphere Reserve in the Kursk Region of Russia. The transport schemes tested are diffusion, advection and both diffusion and advection. They are coupled to three different formulations of soil carbon decomposition kinetics. The first formulation is a first order kinetics widely used in global SOC decomposition models; the second one, so-called "priming" model, links SOC decomposition rate to the amount of fresh organic matter, representing the substrate interactions. The last one is also a first order kinetics, but SOC is split into two pools. Field data are from a set of three bare fallow plots where soil received no input during the past 20, 26 and 58 yr, respectively. Parameters of the models were optimised using a Bayesian method. The best results are obtained when SOC decomposition is assumed to be controlled by fresh organic matter (i.e., the priming model). In comparison to the first-order kinetic model, the priming model reduces the overestimation in the deep layers. We also observed that the transport scheme that improved the fit with the data depended on the soil carbon mineralisation formulation chosen. When soil carbon decomposition was modelled to depend on the fresh organic matter amount, the transport mechanism which improved best the fit to the SOC profile data was the model representing both advection and diffusion. Interestingly, the older the bare fallow is, the lesser the need for diffusion is, suggesting that stabilised carbon may not be transported within the profile by the same mechanisms than more labile carbon.

CO ₂ production in soils responds strongly to changes in temperature and moisture, but the magnitude of such responses at different timescales remains difficult to predict. Knowledge of the mechanisms leading to the often observed interactions in the effects of these drivers on soil CO ₂ emissions is especially limited. Here we test the ability of different soil carbon models to simulate responses measured in soils incubated at a range of moisture levels and cycled through 5, 20, and 35 ^∘ C. We applied parameter optimization methods while modifying two structural components of models: (1) the reaction kinetics of decomposition and uptake and (2) the functions relating fluxes to soil moisture. We found that the observed interactive patterns were best simulated by a model using Michaelis–Menten decomposition kinetics combined with diffusion of dissolved carbon (C) and enzymes. In contrast, conventional empirical functions that scale decomposition rates directly were unable to properly simulate the main observed interactions. Our best model was able to explain 87 % of the variation in the data. Model simulations revealed a central role of Michaelis–Menten kinetics as a driver of temperature sensitivity variations as well as a decoupling of decomposition and respiration C fluxes in the short and mid-term, with general sensitivities to temperature and moisture being more pronounced for respiration. Sensitivity to different model parameters was highest for those affecting diffusion limitations, followed by activation energies, the Michaelis–Menten constant, and carbon use efficiency. Testing against independent data strongly validated the model ( R ² =0.99) and highlighted the importance of initial soil C pool conditions. Our results demonstrate the importance of model structure and the central role of diffusion and reaction kinetics for simulating and understanding complex dynamics in soil C.