A model based on Rock-Eval thermal analysis to quantify the size of the centennially persistent organic carbon pool in temperate soils

Abstract

Abstract. Changes in global soil carbon stocks have considerable potential to influence the course of future climate change. However, a portion of soil organic carbon (SOC) has a very long residence time (> 100 years) and may not contribute significantly to terrestrial greenhouse gas emissions during the next century. The size of this persistent SOC reservoir is presumed to be large. Consequently, it is a key parameter required for the initialization of SOC dynamics in ecosystem and Earth system models, but there is considerable uncertainty in the methods used to quantify it. Thermal analysis methods provide cost-effective information on SOC thermal stability that has been shown to be qualitatively related to SOC biogeochemical stability. The objective of this work was to build the first quantitative model of the size of the centennially persistent SOC pool based on thermal analysis. We used a unique set of 118 archived soil samples from four agronomic experiments in northwestern Europe with long-term bare fallow and non-bare fallow treatments (e.g., manure amendment, cropland and grassland) as a sample set for which estimating the size of the centennially persistent SOC pool is relatively straightforward. At each experimental site, we estimated the average concentration of centennially persistent SOC and its uncertainty by applying a Bayesian curve-fitting method to the observed declining SOC concentration over the duration of the long-term bare fallow treatment. Overall, the estimated concentrations of centennially persistent SOC ranged from 5 to 11 g C kg −1 of soil (lowest and highest boundaries of four 95 % confidence intervals). Then, by dividing the site-specific concentrations of persistent SOC by the total SOC concentration, we could estimate the proportion of centennially persistent SOC in the 118 archived soil samples and the associated uncertainty. The proportion of centennially persistent SOC ranged from 0.14 (standard deviation of 0.01) to 1 (standard deviation of 0.15). Samples were subjected to thermal analysis by Rock-Eval 6 that generated a series of 30 parameters reflecting their SOC thermal stability and bulk chemistry. We trained a nonparametric machine-learning algorithm (random forests multivariate regression model) to predict the proportion of centennially persistent SOC in new soils using Rock-Eval 6 thermal parameters as predictors. We evaluated the model predictive performance with two different strategies. We first used a calibration set (n = 88) and a validation set (n = 30) with soils from all sites. Second, to test the sensitivity of the model to pedoclimate, we built a calibration set with soil samples from three out of the four sites (n = 84). The multivariate regression model accurately predicted the proportion of centennially persistent SOC in the validation set composed of soils from all sites (R 2 = 0.92, RMSEP = 0.07, n = 30). The uncertainty of the model predictions was quantified by aPublished by Copernicus Publications on behalf of the European Geosciences Union. L. Cécil...

Ramped thermal analysis for isolating biologically meaningful soil organic matter fractions with distinct residence times

“…While research suggests that more complex organic molecules have higher thermal stability (Lopez-Capel et al, 2005;Yang et al, 2006), contradictory results have also been reported (Rovira et al, 2008). Several studies have also found good correlations between thermal stability indices and biological stability (Peltre et al, 2013;Soucémarianadin et al, 2018) and model-derived stable C pools (Cécillon et al, 2018). However, other studies have found that new carbon preferentially flowed into more thermally stable fractions (Helfrich et al, 2010;Schiedung et al, 2017), suggesting that the relationship between thermal stability and SOM cycling concepts may not be straightforward.…”

DRIFTS band areas as measured pool size proxy to reduce parameter uncertainty in soil organic matter models

“…However, our comparison shows that models with similar structure come to similar conclusions for SOM turnover. For example, the one-pool model in Clifford et al (2014) was quite similar in turnover rates to that in Luo et al (2016) but does not match well with two-pool models. Then again, the rates for the two-pool models of this study, and the studies by Ahrens et al (2014) and Hararuk et al (2017), were very similar in their minima and maxima, for both the slow and fast SOM pools, which shows that only models with a similar number of pools and transformations could be compared.…”

“…The predictions of mechanistic models usually fail to account for the three main statistical uncertainties in (1) inputs, (2) scientific judgments resulting in different model setups and (3) driving data (Wattenbach et al, 2006). However, with a Bayesian calibration framework such as that implemented in UCODE 2014, almost any model can be made probabilistic, so uncertainties in parameters and outputs can be assessed, even for projections into the future (Clifford et al, 2014). As this study focused on Bayesian calibration and we used an established model, we mainly address parameter uncertainty, although input uncertainty was also included through the weighting process.…”

“…Optimized turnover rates and humification efficiency of this study (using the combined site analysis with original weighting and the DSI) compared to other Bayesian calibrations and standard values of commonly used models. Turnover rates of other models were normalized to the Daisy standard of 10 • C using an exponential equation (an exception was Clifford et al, 2014, where no temperature was given).…”

“…Portion of fast to slow pool = humification efficiency (dimensionless) et al(2014),Bruun et al (2003),Clifford et al (2014), Hararuk et al(2017),Luo et al (2016),Mueller et al (1997) andParton et al (1993) * Clifford et al (2014). did not specify a base temperature for their model.…”