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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

van Oijen, M., Cameron, D. R., Butterbach-Bahl, K., Farahbakhshazad, N., Jansson, P.-E., Kiese, R., Rahn, K.-H., Werner, C., & Yeluripati, J. B. (2011). A Bayesian framework for model calibration, comparison and analysis: Application to four models for the biogeochemistry of a Norway spruce forest. Agricultural and Forest Meteorology, 151, 1609-1621. doi:10.1016/j.agrformet.2011.06.017.

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アイテムのパーマリンク: https://hdl.handle.net/11858/00-001M-0000-0014-779D-1 版のパーマリンク: https://hdl.handle.net/11858/00-001M-0000-0014-9E4C-C
資料種別: 学術論文

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BPR047.pdf (出版社版), 775KB
 
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BPR047.pdf
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制限付き (Max Planck Institute for Biogeochemistry, MJBK; )
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application/pdf
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 作成者:
van Oijen, M.1, 著者
Cameron, D. R., 著者
Butterbach-Bahl, K., 著者
Farahbakhshazad, N., 著者
Jansson, P.-E., 著者
Kiese, R., 著者
Rahn, K.-H., 著者
Werner, C., 著者
Yeluripati, J. B., 著者
所属:
1External Organizations, ou_persistent22              

内容説明

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 要旨: Four different parameter-rich process-based models of forest biogeochemistry were analysed in a Bayesian framework consisting of three operations: (1) Model calibration, (2) Model comparison, (3) Analysis of model–data mismatch. Data were available for four output variables common to the models: soil water content and emissions of N2O, NO and CO2. All datasets consisted of time series of daily measurements. Monthly averages and quantiles of the annual frequency distributions of daily emission rates were calculated for comparison with equivalent model outputs. This use of the data at model-appropriate temporal scale, together with the choice of heavy-tailed likelihood functions that accounted for data uncertainty through random and systematic errors, helped prevent asymptotic collapse of the parameter distributions in the calibration. Model behaviour and how it was affected by calibration was analysed by quantifying the normalised RMSE and r2 for the different output variables, and by decomposition of the MSE into contributions from bias, phase shift and variance error. The simplest model, BASFOR, seemed to underestimate the temporal variance of nitrogenous emissions even after calibration. The model of intermediate complexity, DAYCENT, simulated the time series well but with large phase shift. COUP and MoBiLE-DNDC were able to remove most bias through calibration. The Bayesian framework was shown to be effective in improving the parameterisation of the models, quantifying the uncertainties in parameters and outputs, and evaluating the different models. The analysis showed that there remain patterns in the data – in particular infrequent events of very high nitrogenous emission rate – that are unexplained by any of the selected forest models and that this is unlikely to be due to incorrect model parameterisation.

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 日付: 2011
 出版の状態: 出版
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 識別子（DOI, ISBNなど）: その他: BPR047
DOI: 10.1016/j.agrformet.2011.06.017
 学位: -

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出版物 1

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出版物名: Agricultural and Forest Meteorology
種別: 学術雑誌
 著者・編者:
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出版社, 出版地: Amsterdam : Elsevier
ページ: - 巻号: 151 通巻号: - 開始・終了ページ: 1609 - 1621 識別子（ISBN, ISSN, DOIなど）: ISSN: 0168-1923
CoNE: https://pure.mpg.de/cone/journals/resource/954928468040