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| Titel |
Information content of incubation experiments for inverse estimation of pools in the Rothamsted carbon model: a Bayesian approach |
| VerfasserIn |
Benedikt Scharnagl, Jasper A. Vrugt, Harry Vereecken, Michael Herbst |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250041962
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| Zusammenfassung |
| Turnover of soil organic matter is usually described with multi-compartment models.
However, a major drawback of these models is that the conceptually defined compartments
(or pools) do not necessarily correspond to measurable soil organic carbon (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
tested the usefulness and applicability of inverse modeling to derive the various
carbon pool sizes in the Rothamsted carbon model (ROTHC) using a synthetic
time series of mineralization rates from laboratory incubation. To appropriately
account for data and model uncertainty we considered a Bayesian approach using
the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM)
algorithm. This Markov chain Monte Carlo scheme derives the posterior probability
density distribution of the initial pool sizes at the start of incubation from observed
mineralization rates. We used the Kullback-Leibler divergence to quantify the information
contained in the data and to illustrate the effect of increasing incubation times on the
reliability of the pool size estimates. Our results show that measured mineralization
rates generally provide sufficient information to reliably estimate the sizes of all
active pools in the ROTHC model. However, with about 900Â days of incubation,
these experiments are excessively long. The use of prior information on microbial
biomass provided a way forward to significantly reduce uncertainty and required
duration of incubation to about 600Â days. Explicit consideration of model parameter
uncertainty in the estimation process further impaired the identifiability of initial pools,
especially for the more slowly decomposing pools. Our illustrative case studies show
how Bayesian inverse modeling can be used to provide important insights into the
information content of incubation experiments. Moreover, the outcome of this virtual
experiment helps to explain the results of related real-world studies on SOC dynamics. |
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