Bayesian calibration of a model describing carbon, water and heat fluxes for a Swedish boreal forest stand

Svensson, Magnus; Jansson, Per‐Erik; Gustafsson, David; Kleja, Dan Berggren; Langvall, Ola; Lindroth, Anders

doi:10.1016/j.ecolmodel.2008.01.001

Cited by 53 publications

(36 citation statements)

References 22 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%

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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%

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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“…Bayesian statistics are a useful tool for the parameterization and analyses of mechanistic models such as ANAFORE, as has been found in a few previous studies (Svensson et al 2008, Van Oijen et al 2005. Although optimization of the input parameters yields high correlation between measured and simulated data, the Bayesian analyses indicates the high uncertainty.…”

Section: Discussionmentioning

confidence: 97%

“…Although in other environmental sciences Bayesian approach is often used, in forest research only a limited number of studies have been published (Van Oijen et al 2005, Svensson et al 2008. In a Bayesian approach, all input and outputs consist of probability distributions.…”

Section: Bayesian Optimizationmentioning

confidence: 99%

“…Therefore, high gross photosynthesis and dark respiration will yield as good a fit as low values for both parameters. In a study by Svensson et al (2008) where data on C-fluxes were used for the parameterization of a forest growth model, the uncertainty of most of the parameters was reduced. Inclusion of a limited number of specific measurements (leaf N, specific leaf area S LA ) could improve our results significantly.…”

Section: Posterior Versus Prior Distributionsmentioning

confidence: 99%

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Using the process-based stand model ANAFORE including Bayesian optimisation to predict wood quality and quantity and their uncertainty in Slovenian beech

Deckmyn¹,

Mali²,

Kraigher³

et al. 2009

Silva Fenn.

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The purpose of this study was to expand an existing semi-mechanistic forest model, ANA-FORE (ANAlysing Forest Ecosystems), to allow for the prediction of log quality and the accompanying uncertainty as influenced by climate and management. The forest stand is described as consisting of trees of different cohorts, either of the same or of different species (deciduous or coniferous). In addition to photosynthesis, transpiration, total growth and yield, the model simulates the daily evolution in vessel biomass and radius, parenchyma and branch development. From these data early and latewood biomass, wood tissue composition, knot formation and density are calculated. The new version presented here, includes the description of log quality, including red heart formation of beeches.A Bayesian optimisation routine for the species parameters was added to the stand model. From a given range of input parameters (prior), the model calculates an optimised range for the parameters (posterior) based on given output data, as well as an uncertainty on the predicted values.A case study was performed for Slovenian beech forests to illustrate the main model functioning and more in particular the simulation of the wood quality. The results indicate that the ANAFORE model is a useful tool for analyzing wood quality development and forest ecosystem functioning in response to management, climate and stand characteristics. However, the Bayesian optimization showed that the remaining uncertainty on the input parameters for the chosen stand was very large, due to the large number of input parameters in comparison to the limited stand data.

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Forest‐Management Modelling

Twery

Weiskittel

2013

Environmental Modelling

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Bayesian calibration of a model describing carbon, water and heat fluxes for a Swedish boreal forest stand

Cited by 53 publications

References 22 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

Using the process-based stand model ANAFORE including Bayesian optimisation to predict wood quality and quantity and their uncertainty in Slovenian beech

Forest‐Management Modelling

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Bayesian calibration of a model describing carbon, water and heat fluxes for a Swedish boreal forest stand

Cited by 53 publications

References 22 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

Using the process-based stand model ANAFORE including Bayesian optimisation to predict wood quality and quantity and their uncertainty in Slovenian beech

Forest‐Management Modelling

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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