Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Journal topic
Volume 13, issue 10
Biogeosciences, 13, 3003–3019, 2016
https://doi.org/10.5194/bg-13-3003-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Biogeosciences, 13, 3003–3019, 2016
https://doi.org/10.5194/bg-13-3003-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 23 May 2016

Research article | 23 May 2016

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

Lorenzo Menichetti1, Thomas Kätterer2, and Jens Leifeld1 Lorenzo Menichetti et al.
  • 1Agroscope, Climate/Air Pollution Group, Reckenholzstrasse 191, 8046 Zürich, Switzerland
  • 2Department of Ecology, Swedish University of Agricultural Sciences (SLU), P.O. Box 7044, 75007 Uppsala, Sweden

Abstract. Soil organic carbon (SOC) dynamics result from different interacting processes and controls on spatial scales from sub-aggregate to pedon to the whole ecosystem. These complex dynamics are translated into models as abundant degrees of freedom. This high number of not directly measurable variables and, on the other hand, very limited data at disposal result in equifinality and parameter uncertainty.

Carbon radioisotope measurements are a proxy for SOC age both at annual to decadal (bomb peak based) and centennial to millennial timescales (radio decay based), and thus can be used in addition to total organic C for constraining SOC models. By considering this additional information, uncertainties in model structure and parameters may be reduced.

To test this hypothesis we studied SOC dynamics and their defining kinetic parameters in the Zürich Organic Fertilization Experiment (ZOFE) experiment, a > 60-year-old controlled cropland experiment in Switzerland, by utilizing SOC and SO14C time series. To represent different processes we applied five model structures, all stemming from a simple mother model (Introductory Carbon Balance Model – ICBM): (I) two decomposing pools, (II) an inert pool added, (III) three decomposing pools, (IV) two decomposing pools with a substrate control feedback on decomposition, (V) as IV but with also an inert pool. These structures were extended to explicitly represent total SOC and 14C pools.

The use of different model structures allowed us to explore model structural uncertainty and the impact of 14C on kinetic parameters. We considered parameter uncertainty by calibrating in a formal Bayesian framework.

By varying the relative importance of total SOC and SO14C data in the calibration, we could quantify the effect of the information from these two data streams on estimated model parameters. The weighing of the two data streams was crucial for determining model outcomes, and we suggest including it in future modeling efforts whenever SO14C data are available.

The measurements and all model structures indicated a dramatic decline in SOC in the ZOFE experiment after an initial land use change in 1949 from grass- to cropland, followed by a constant but smaller decline. According to all structures, the three treatments (control, mineral fertilizer, farmyard manure) we considered were still far from equilibrium. The estimates of mean residence time (MRT) of the C pools defined by our models were sensitive to the consideration of the SO14C data stream. Model structure had a smaller effect on estimated MRT, which ranged between 5.9 ± 0.1 and 4.2 ± 0.1 years and 78.9 ± 0.1 and 98.9 ± 0.1 years for young and old pools, respectively, for structures without substrate interactions.

The simplest model structure performed the best according to information criteria, validating the idea that we still lack data for mechanistic SOC models. Although we could not exclude any of the considered processes possibly involved in SOC decomposition, it was not possible to discriminate their relative importance.

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Short summary
Soil organic carbon dynamics are crucial for the global greenhouse gas balance, but their complexity is difficult to model and understand. We therefore often rely on radiocarbon measurements for calibrating models, but their effect on our understanding of the processes is still unclear. We calibrated five model structures on data from a long-term Swiss field experiment in a Bayesian framework to assess the effect of radiocarbon on the parameter and structural uncertainty of a soil carbon model.
Soil organic carbon dynamics are crucial for the global greenhouse gas balance, but their...
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