TY  - JOUR
A1  - Rahn, Karl-Heinz
A1  - Werner, Christian
A1  - Kiese, Ralf
A1  - Haas, Edwin
A1  - Butterbach-Bahl, Klaus
T1  - Parameter-induced uncertainty quantification of soil N 2 O, NO and CO 2 emission from Höglwald spruce forest (Germany) using the LandscapeDNDC model [Discussion paper]
T2  - Biogeosciences discussions
N2  - Assessing the uncertainties of simulation results of ecological models is becoming of increasing importance, specifically if these models are used to estimate greenhouse gas emissions at 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 showed that by means of the joined 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 the 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 (Residual mean squared error (RMSE) NO: 2.5 to 21.3 g N ha−1 d−1, N2O: 0.2 to 21.4 g N ha−1 d−1, CO2: 5.8 to 12.6 kg C ha−1 d−1). However, LandscapeDNDC simulations were sometimes limited to accurately predict observed seasonal variations in fluxes.
Y1  - 2012
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/27144
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-271446
SN  - 1810-6277
N1  - © Author(s) 2012. This work is distributed under the Creative Commons Attribution 3.0 License.
VL  - 9
SP  - 5249
EP  - 5286
PB  - European Geosciences Union
CY  - Katlenburg-Lindau
ER  -