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| Titel |
Parameter-induced uncertainty quantification of soil N2O, NO and CO2 emission from Höglwald spruce forest (Germany) using the LandscapeDNDC model |
| VerfasserIn |
K.-H. Rahn, C. Werner, R. Kiese, E. Haas, K. Butterbach-Bahl |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 9, no. 10 ; Nr. 9, no. 10 (2012-10-17), S.3983-3998 |
| Datensatznummer |
250007332
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| Publikation (Nr.) |
 copernicus.org/bg-9-3983-2012.pdf |
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| Zusammenfassung |
Assessing the uncertainties of simulation results of ecological
models is becoming increasingly important, specifically if these
models are used to estimate greenhouse gas emissions on site to
regional/national levels. Four general sources of uncertainty
effect the outcome of process-based models: (i) uncertainty of
information used to initialise and drive the model, (ii) uncertainty
of model parameters describing specific ecosystem processes, (iii)
uncertainty of the model structure, and (iv) accurateness of
measurements (e.g., soil-atmosphere greenhouse gas exchange) which
are used for model testing and development.
The aim of our study was to assess the simulation uncertainty of the
process-based biogeochemical model LandscapeDNDC. For this we set up
a Bayesian framework using a Markov Chain Monte Carlo (MCMC) method,
to estimate the joint model parameter distribution. Data for model
testing, parameter estimation and uncertainty assessment were taken
from observations of soil fluxes of nitrous oxide (N2O),
nitric oxide (NO) and carbon dioxide (CO2) as observed over
a 10 yr period at the spruce site of the Höglwald Forest,
Germany. By running four independent Markov Chains in parallel with
identical properties (except for the parameter start values), an
objective criteria for chain convergence developed by
Gelman et al. (2003) could be used.
Our approach shows that by means of the joint parameter
distribution, we were able not only to limit the parameter space and
specify the probability of parameter values, but also to assess the
complex dependencies among model parameters used for simulating soil
C and N trace gas emissions. This helped to improve the
understanding of the behaviour of the complex LandscapeDNDC model
while simulating soil C and N turnover processes and associated C
and N soil-atmosphere exchange.
In a final step the parameter distribution of the most sensitive
parameters determining soil-atmosphere C and N exchange were used to
obtain the parameter-induced uncertainty of simulated N2O,
NO and CO2 emissions. These were compared to observational
data of an calibration set (6 yr) and an independent validation
set of 4 yr.
The comparison showed that most of the annual observed trace gas
emissions were in the range of simulated values and were predicted
with a high certainty (Root-mean-squared error (RMSE) NO: 2.4 to
18.95 g N ha−1 d−1, N2O: 0.14 to
21.12 g N ha−1 d−1, CO2: 5.4 to
11.9 kg C ha−1 d−1). However, LandscapeDNDC
simulations were sometimes still limited to accurately predict observed
seasonal variations in fluxes. |
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