| Volumes and Issues Contents of Issue 5 |
www.biogeosciences-discuss.net/6/9331/2009/
doi:10.5194/bgd-6-9331-2009
© Author(s) 2009. This work is distributed
under the Creative Commons Attribution 3.0 License.
Information content of incubation experiments for inverse estimation of pools sizes in the Rothamsted carbon model: a Bayesian approach
1Agrosphere Institute (ICG-4), Forschungszentrum Jülich, 52425 Jülich, Germany
2Center for Nonlinear Studies, Los Alamos National Lab., Los Alamos, NM 87545, USA
3Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, The Netherlands
Abstract. Turnover of soil organic matter (SOM) is usually described with multi-compartment models. A model compartment (or pool) contains all carbon compounds with similar functional properties, such as decomposition rate and partitioning of decomposition products. These functionally defined carbon pools do not necessarily correspond to measurable (SOC) fractions in real practice. This not only impairs our ability to rigorously evaluate SOC models, but also makes it difficult to derive accurate initial states. In this study, we test the usefulness and applicability of inverse modeling to derive the various carbon pool sizes in the Rothamsted carbon model (ROTHC) using observed mineralization rate data during incubation of soil samples in the laboratory. In the last decade, inverse modeling has found widespread application and use for environmental model calibration, but this methodology has not yet been tested for assessing carbon pools in multi-compartment SOC models. To appropriately consider data and model uncertainty we consider a Bayesian approach using the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. This Markov Chain Monte Carlo (MCMC) scheme derives the posterior probability density distribution of the initial pool sizes at the start of incubation from measured mineralization rates. Our results show that measured mineralization rates generally provide sufficient information to reliably estimate the sizes of all active carbon pools in the ROTHC model. However, for soils with slow and intermediate carbon turnover an excessively long incubation time is required to appropriately constrain all carbon pools. The explicit use of prior information on microbial biomass provides a way forward to significantly reduce uncertainty and required duration of incubation. Our illustrative case studies show how Bayesian inverse modeling can be used to provide important insights into the information content of incubation experiments for assessing SOC turnover and dynamics.
Discussion Paper (PDF, 1600 KB) Interactive Discussion (Closed, 8 Comments) Final Revised Paper (BG)
Citation: Scharnagl, B., Vrugt, J. A., Vereecken, H., and Herbst, M.: Information content of incubation experiments for inverse estimation of pools sizes in the Rothamsted carbon model: a Bayesian approach, Biogeosciences Discuss., 6, 9331-9357, doi:10.5194/bgd-6-9331-2009, 2009. Bibtex EndNote Reference Manager XML
