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| Titel |
Application of the Bayesian calibration methodology for the parameter estimation in CoupModel |
| VerfasserIn |
Y. Conrad, N. Fohrer |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7340
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| Digitales Dokument |
URL |
| Erschienen |
In: Transdisciplinary concepts and modelling strategies for the assessment of complex environmental systems ; Nr. 21 (2009-08-10), S.13-24 |
| Datensatznummer |
250014525
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| Publikation (Nr.) |
 copernicus.org/adgeo-21-13-2009.pdf |
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| Zusammenfassung |
| This study provides results for the optimization strategy of highly
parameterized models, especially with a high number of unknown input
parameters and joint problems in terms of sufficient parameter space.
Consequently, the uncertainty in model parameterization and measurements
must be considered when highly variable nitrogen losses, e.g. N leaching,
are to be predicted. The Bayesian calibration methodology was used to
investigate the parameter uncertainty of the process-based CoupModel.
Bayesian methods link prior probability distributions of input parameters to
likelihood estimates of the simulation results by comparison with measured
values. The uncertainty in the updated posterior parameters can be used to
conduct an uncertainty analysis of the model output. A number of 24 model
variables were optimized during 20 000 simulations to find the "optimum"
value for each parameter. The likelihood was computed by comparing
simulation results with observed values of 23 output variables including
soil water contents, soil temperatures, groundwater level, soil mineral
nitrogen, nitrate concentrations below the root zone, denitrification and
harvested carbon from grassland plots in Northern Germany for the period
1997–2002. The posterior parameter space was sampled with the Markov Chain
Monte Carlo approach to obtain plot-specific posterior parameter
distributions for each system. Posterior distributions of the parameters
narrowed down in the accepted runs, thus uncertainty decreased. Results from
the single-plot optimization showed a plausible reproduction of soil
temperatures, soil water contents and water tensions in different soil
depths for both systems. The model performed better for these abiotic
system properties compared to the results for harvested carbon and soil
mineral nitrogen dynamics. The high variability in modeled nitrogen
leaching showed that the soil nitrogen conditions are highly uncertain
associated with low modeling efficiencies. Simulated nitrate leaching was
compared to more general, site-specific estimations, indicating a higher
leaching during the seepage periods for both simulated grassland systems. |
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