Bayesian calibration method used to elucidate carbon turnover in forest on drained organic soil

Klemedtsson, Leif; Jansson, Per‐Erik; Gustafsson, David; Weslien, Per; Arnold, Karin von; Ernfors, Maria; Langvall, Ola; Lindroth, Anders

doi:10.1007/s10533-007-9169-0

Cited by 34 publications

(27 citation statements)

References 31 publications

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“…The applicability of the illustrated method to complex ecological models was also demonstrated in previous studies (e.g. van Oijen et al, 2005;Svensson et al, 2008;Klemedtsson et al, 2008;Lehuger et al, 2009).…”

Section: Discussionsupporting

confidence: 70%

“…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%

“…The used parameter value is either the true value or it is not (see Ellison, 1996). Therefore, a Bayesian approach is needed (Clark, 2005;van Oijen et al, 2005;Klemedtsson et al, 2008;Gelman et al, 2003;Reinds et al, 2008;Lehuger et al, 2009) since it models the parameter vector θ as a random vector, which allows a direct quantification of the probability of a certain parameter realisation/range.…”

Section: Bayesian Calibrationmentioning

confidence: 99%

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Parameter-induced uncertainty quantification of soil N2O, NO and CO2 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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Section: Discussionsupporting

confidence: 70%

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

confidence: 99%

Section: Bayesian Calibrationmentioning

confidence: 99%

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Parameter-induced uncertainty quantification of soil N2O, NO and CO2 emission from Höglwald spruce forest (Germany) using the LandscapeDNDC model

et al. 2012

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“…tion was to force the model to simulate tree biomass and soil C / N ratio precisely to better constrain posterior parameter distributions for the respective model approach and site. The Bayesian calibration as applied in this study is briefly described below; however, for a detailed description of the general methodology see Klemedtsson et al (2008) and van Oijen et al (2005), for example.…”

Section: Observational Constraintsmentioning

confidence: 99%

Simulating ectomycorrhiza in boreal forests: implementing ectomycorrhizal fungi model MYCOFON in CoupModel (v5)

Meyer

Jansson

et al. 2018

Geosci. Model Dev.

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Abstract. The symbiosis between plants and Ectomycorrhizal fungi (ECM) is shown to considerably influence the carbon (C) and nitrogen (N) fluxes between the soil, rhizosphere, and plants in boreal forest ecosystems. However, ECM are either neglected or presented as an implicit, undynamic term in most ecosystem models, which can potentially reduce the predictive power of models.In order to investigate the necessity of an explicit consideration of ECM in ecosystem models, we implement the previously developed MYCOFON model into a detailed process-based, soil-plant-atmosphere model, Coup-MYCOFON, which explicitly describes the C and N fluxes between ECM and roots. This new Coup-MYCOFON model approach (ECM explicit) is compared with two simpler model approaches: one containing ECM implicitly as a dynamic uptake of organic N considering the plant roots to represent the ECM (ECM implicit), and the other a static N approach in which plant growth is limited to a fixed N level (nonlim). Parameter uncertainties are quantified using Bayesian calibration in which the model outputs are constrained to current forest growth and soil C / N ratio for four forest sites along a climate and N deposition gradient in Sweden and simulated over a 100-year period.The "nonlim" approach could not describe the soil C / N ratio due to large overestimation of soil N sequestration but simulate the forest growth reasonably well. The ECM "implicit" and "explicit" approaches both describe the soil C / N ratio well but slightly underestimate the forest growth. The implicit approach simulated lower litter production and soil respiration than the explicit approach. The ECM explicit Coup-MYCOFON model provides a more detailed description of internal ecosystem fluxes and feedbacks of C and N between plants, soil, and ECM. Our modeling highlights the need to incorporate ECM and organic N uptake into ecosystem models, and the nonlim approach is not recommended for future long-term soil C and N predictions. We also provide a key set of posterior fungal parameters that can be further investigated and evaluated in future ECM studies.

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“…The Bayesian calibration methods have been used for optimization of forest ecosystems (Van Oijen et al, 2005;Klemedtsson et al, 2008) or watershed models (Vrugt et al, 2006). Bayesian approaches include probability distributions of model input parameters, based on prior assumptions about their magnitude and uncertainty, combined with likelihood estimates of the model results by comparison with observations for model output variables.…”

Section: Introductionmentioning

confidence: 99%

Application of the Bayesian calibration methodology for the parameter estimation in CoupModel

Conrad

Fohrer

2009

Adv. Geosci.

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Abstract. 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 Correspondence to: Y. Conrad (yconrad@hydrology-uni-kiel.de) uncertain associated with low modeling efficiencies. Simulated nitrate leaching was compared to more general, sitespecific estimations, indicating a higher leaching during the seepage periods for both simulated grassland systems.

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Bayesian calibration method used to elucidate carbon turnover in forest on drained organic soil

Cited by 34 publications

References 31 publications

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

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

Simulating ectomycorrhiza in boreal forests: implementing ectomycorrhizal fungi model MYCOFON in CoupModel (v5)

Application of the Bayesian calibration methodology for the parameter estimation in CoupModel

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Bayesian calibration method used to elucidate carbon turnover in forest on drained organic soil

Cited by 34 publications

References 31 publications

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

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

Simulating ectomycorrhiza in boreal forests: implementing ectomycorrhizal fungi model MYCOFON in CoupModel (v5)

Application of the Bayesian calibration methodology for the parameter estimation in CoupModel

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Product

Resources

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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

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