A Bayesian framework for model calibration, comparison and analysis: Application to four models for the biogeochemistry of a Norway spruce forest

Oijen, Marcel van; Cameron, David R.; Butterbach‐Bahl, Klaus; Farahbakhshazad, Neda; Jansson, Per‐Erik; Kiese, Ralf; Rahn, Karl-Heinz; Werner, Christian; Yeluripati, Jagadeesh

doi:10.1016/j.agrformet.2011.06.017

Cited by 83 publications

(92 citation statements)

References 41 publications

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“…Formal probabilistic methods (i.e., Bayesian inference) are particularly well suited for comparing models with data and making projections that incorporate estimates of uncertainty, so would be particularly attractive for our proposed approach (Kass and Raftery 1995;Berger et al 1999;Kennedy and O'Hagan 2001;Oakley and O'Hagan 2002;Berliner 2003). So far, these have proven computationally unfeasible for the most detailed models (Oreskes et al 1994;Smith and Stern 2011;van Oijen et al 2011;Palmer 2012), but this could be addressed on the short term in a number of ways. For instance, Bayesian emulators of detailed models could be employed to make probabilistic predictions based on limited runs of the computer code (Kennedy and O'Hagan 2001;Oakley and O'Hagan 2002), or the number of details could even be restricted to a level where their suitability could be assessed using Bayesian methods.…”

Section: An Alternative Approachmentioning

confidence: 99%

Changing How Earth System Modeling is Done to Provide More Useful Information for Decision Making, Science, and Society

Smith

Palmer

Purves

et al. 2014

Bulletin of the American Meteorological Society

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Section: An Alternative Approachmentioning

confidence: 99%

Changing How Earth System Modeling is Done to Provide More Useful Information for Decision Making, Science, and Society

Smith

Palmer

Purves

et al. 2014

Bulletin of the American Meteorological Society

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“…Lehuger et al, 2009). To our knowledge van Oijen et al (2011) is the only study so far comparing four process-based biogeochemical forest models within a Bayesian model comparison framework. In contrast to such a model inter-comparison, the aim of this study is to provide deeper insights into the individual parameter uncertainty and calibration of the model LandscapeD-NDC and the subsequent uncertainty of simulated trace gas exchange.…”

Section: K-h Rahn Et Al: Uq Of Soil Ghg Emissions Using Landscapedndcmentioning

confidence: 99%

“…van Oijen et al, 2005). Uncertainty estimates in many modelling studies that investigate the soilatmosphere exchange of trace gases only cover the assessment of uncertainty imposed by input data (e.g.…”

Section: K-h Rahn Et Al: Uq Of Soil Ghg Emissions Using Landscapedndcmentioning

confidence: 99%

“…The Bayesian theorem was used for calibration and uncertainty assessment of parameters of dynamic process-based forest models mainly focusing on carbon turnover (van Oijen et al, 2005;Svensson et al, 2008;Klemedtsson et al, 2008) and more recently also for parameters involved in production, consumption, transport and emissions of soil GHGs (e.g. Lehuger et al, 2009).…”

Section: K-h Rahn Et Al: Uq Of Soil Ghg Emissions Using Landscapedndcmentioning

confidence: 99%

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Parameter-induced uncertainty quantification of soil N<sub>2</sub>O, NO and CO<sub>2</sub> emission from Höglwald spruce forest (Germany) using the LandscapeDNDC model

et al. 2012

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Abstract. 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 processbased 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 (N 2 O), nitric oxide (NO) and carbon dioxide (CO 2 ) 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 N 2 O, NO and CO 2 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 , N 2 O: 0.14 to 21.12 g N ha −1 d −1 , CO 2 : 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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“…The model structure was described by . Papers describing more recent model developments are Van Oijen and Thomson (2010) and Van Oijen et al (2011).…”

Section: Basformentioning

confidence: 99%

Environmental change impacts on the C- and N-cycle of European forests: a model comparison study

et al. 2013

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Forests are important components of the greenhouse gas balance of Europe. There is considerable uncertainty about how predicted changes to climate and nitrogen deposition will perturb the carbon and nitrogen cycles of European forests and thereby alter forest growth, carbon sequestration and N₂O emission. The present study aimed to quantify the carbon and nitrogen balance, including the exchange of greenhouse gases, of European forests over the period 2010–2030, with a particular emphasis on the spatial variability of change. The analysis was carried out for two tree species: European beech and Scots pine. For this purpose, four different dynamic models were used: BASFOR, DailyDayCent, INTEGRATOR and Landscape-DNDC. These models span a range from semi-empirical to complex mechanistic. Comparison of these models allowed assessment of the extent to which model predictions depended on differences in model inputs and structure. We found a European average carbon sink of 0.160 ± 0.020 kgC m⁻² yr⁻¹ (pine) and 0.138 ± 0.062 kgC m⁻² yr⁻¹ (beech) and N₂O source of 0.285 ± 0.125 kgN ha⁻¹ yr⁻¹ (pine) and 0.575 ± 0.105 kgN ha⁻¹ yr⁻¹ (beech). The European average greenhouse gas potential of the carbon sink was 18 (pine) and 8 (beech) times that of the N₂O source. Carbon sequestration was larger in the trees than in the soil. Carbon sequestration and forest growth were largest in central Europe and lowest in northern Sweden and Finland, N. Poland and S. Spain. No single driver was found to dominate change across Europe. Forests were found to be most sensitive to change in environmental drivers where the drivers were limiting growth, where changes were particularly large or where changes acted in concert. The models disagreed as to which environmental changes were most significant for the geographical variation in forest growth and as to which tree species showed the largest rate of carbon sequestration. Pine and beech forests were found to have differing sensitivities to environmental change, in particular the response to changes in nitrogen and precipitation, with beech forest more vulnerable to drought. There was considerable uncertainty about the geographical location of N₂O emissions. Two of the models BASFOR and LandscapeDNDC had largest emissions in central Europe where nitrogen deposition and soil nitrogen were largest, whereas the two other models identified different regions with large N₂O emission. N₂O emissions were found to be larger from beech than pine forests and were found to be particularly sensitive to forest growth

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A Bayesian framework for model calibration, comparison and analysis: Application to four models for the biogeochemistry of a Norway spruce forest

Cited by 83 publications

References 41 publications

Changing How Earth System Modeling is Done to Provide More Useful Information for Decision Making, Science, and Society

Changing How Earth System Modeling is Done to Provide More Useful Information for Decision Making, Science, and Society

Parameter-induced uncertainty quantification of soil N<sub>2</sub>O, NO and CO<sub>2</sub> emission from Höglwald spruce forest (Germany) using the LandscapeDNDC model

Environmental change impacts on the C- and N-cycle of European forests: a model comparison study

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A Bayesian framework for model calibration, comparison and analysis: Application to four models for the biogeochemistry of a Norway spruce forest

Cited by 83 publications

References 41 publications

Changing How Earth System Modeling is Done to Provide More Useful Information for Decision Making, Science, and Society

Changing How Earth System Modeling is Done to Provide More Useful Information for Decision Making, Science, and Society

Parameter-induced uncertainty quantification of soil N&lt;sub&gt;2&lt;/sub&gt;O, NO and CO&lt;sub&gt;2&lt;/sub&gt; emission from Höglwald spruce forest (Germany) using the LandscapeDNDC model

Environmental change impacts on the C- and N-cycle of European forests: a model comparison study

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Parameter-induced uncertainty quantification of soil N<sub>2</sub>O, NO and CO<sub>2</sub> emission from Höglwald spruce forest (Germany) using the LandscapeDNDC model