Bayesian calibration of a soil organic carbon model using Δ<sup>14</sup>C measurements of soil organic carbon and heterotrophic respiration as joint constraints

Abstract

Abstract. Soils of temperate forests store significant amounts of organic matter and are considered to be net sinks of atmospheric CO 2 . Soil organic carbon (SOC) turnover has been studied using the 14 C values of bulk SOC or different SOC fractions as observational constraints in SOC models. Further, the 14 C values of CO 2 that evolved during the incubation of soil and roots have been widely used together with 14 C of total soil respiration to partition soil respiration into heterotrophic respiration (HR) and rhizosphere respiration. However, these data have not been used as joint observational constraints to determine SOC turnover times. Thus, we focus on (1) how different combinations of observational constraints help to narrow estimates of turnover times and other parameters of a simple two-pool model, the Introductory Carbon Balance Model (ICBM); (2) whether relaxing the steady-state assumption in a multiple constraints approach allows the source/sink strength of the soil to be determined while estimating turnover times at the same time. To this end ICBM was adapted to model SOC and SO 14 C in parallel with litterfall and the 14 C of litterfall as driving variables. The 14 C of the atmosphere with its prominent bomb peak was used as a proxy for the 14 C of litterfall. Data from three spruce-dominated temperate forests in Germany and the USA (Coulissenhieb II, Solling D0 and Howland Tower site) were used to estimate the parameters of ICBM via Bayesian calibration. Key findings are as follows: (1) the joint use of all four observational constraints (SOC stock and its 14 C, HR flux and its 14 C) helped to considerably narrow turnover times of the young pool (primarily by 14 C of HR) and the old pool (primarily by 14 C of SOC). Furthermore, the joint use of all observational constraints made it possible to constrain the humification factor in ICBM, which describes the fraction of the annual outflux from the young pool that enters the old pool. The Bayesian parameter estimation yielded the following turnover times (mean ± standard deviation) for SOC in the young pool: Coulissenhieb II 1.1 ± 0.5 years, Solling D0 5.7 ± 0.8 years and Howland Tower 0.8 ± 0.4 years. Turnover times for the old pool were 377 ± 61 years (Coulissenhieb II), 313 ± 66 years (Solling D0) and 184 ± 42 years (Howland Tower), respectively. (2) At all three sites the multiple constraints approach was not able to determine if the soil has been losing or storing carbon. Nevertheless, the relaxed steady-state assumption hardly introduced any additional uncertainty for the other parameter estimates. Overall the results suggest that using 14 C data from more than one carbon pool or flux helps to better constrain SOC models.

Predicting the long‐term fate of buried organic carbon in colluvial soils

“…More data on the fate of deposited SOC are necessary to investigate how both SOC burial and SOC response to management changes vary globally. In particular, more detailed information on SOC pools and isotopic composition [e.g., Ahrens et al, 2014;Skjemstad et al, 2004] could provide a more stringent test of model performance.…”

Parametrization consequences of constraining soil organic matter models by total carbon and radiocarbon using long-term field data

“…Braakhekke et al (2014), using a soil profile model, found that the addition of SO 14 C data as a new constraint produced an increase in the uncertainty of the SOC stocks in the individual layers, while improved just marginally the total SOC stock estimate. Ahrens et al (2014) utilized SO 14 C data to constrain an isotopically explicit single layer model in a situation where data about SOC kinetics were scarce. In that case the problem of model initialization was partially solved with additional information coming from 14 C, but the high uncertainty of the considered system did not make it possible to determine if one site was losing or gaining carbon, and the strong interaction between MRT and deviation from the steady state made evident a trade-off between estimates with and without using SO 14 C data.…”

“…One of the possible reasons for the recorded discrepancies in the estimates from models conditioned with and without SO 14 C data might be the absence of microbial dynamics in SOC stabilization (Riley et al, 2014). Ahrens et al (2015), with a rather mechanistic model, recently suggested that a control on biologically mediated depolymerization can explain alone some of the observed discrepancies. But the performances of structure IV and V on our data set, lower in terms of AIC compared to the simpler structures I and II, did not allow us to confirm such a hypothesis.…”

“…Yet a few studies have considered SO 14 C data within an uncertainty analysis framework. Braakhekke et al (2014) and Ahrens et al (2014) both considered model uncertainty, but focused on a single model structure. However, both pa-rameter uncertainty and structural uncertainty are significant problems endemic to environmental models (Beven, 2002).…”

Constraining a coupled erosion and soil organic carbon model using hillslope‐scale patterns of carbon stocks and pool composition

“…The definition of these rate coefficients and the functions that relate soil organic carbon (SOC) turnover to environmental conditions have typically been obtained by observations on changes in total carbon stocks [Katterer and Andren, 1999]. More recently, 14 C content of bulk SOC or different SOC fractions has been successfully used as additional observational constraints in SOC models to calculate turnover times of SOC [Ahrens et al, 2014;Trumbore, 1993]. Others have successfully related conceptual carbon pools to measurable carbon fractions under specific conditions [Skjemstad et al, 2004;Zimmermann et al, 2007].…”