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Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output

Raj, Rahul; Tol, Christiaan van der; Hamm, Nicholas A.S; Stein, Alfred


Rahul Raj

Christiaan van der Tol

Nicholas A.S Hamm

Alfred Stein


Parameters of a process-based forest growth simulator are difficult or impossible to obtain from field observations. Reliable estimates can be obtained using calibration against observations of output and state variables. In this study, we present a Bayesian framework to calibrate the widely used process-based simulator Biome-BGC against estimates of gross primary production (GPP) data. We used GPP partitioned from flux tower measurements of a net ecosystem exchange over a 55-year-old Douglas fir stand as an example. The uncertainties of both the Biome-BGC parameters and the simulated GPP values were estimated. The calibrated parameters leaf and fine root turnover (LFRT), ratio of fine root carbon to leaf carbon (FRC : LC), ratio of carbon to nitrogen in leaf (C : Nleaf), canopy water interception coefficient (Wint), fraction of leaf nitrogen in RuBisCO (FLNR), and effective soil rooting depth (SD) characterize the photosynthesis and carbon and nitrogen allocation in the forest. The calibration improved the root mean square error and enhanced Nash–Sutcliffe efficiency between simulated and flux tower daily GPP compared to the uncalibrated Biome-BGC. Nevertheless, the seasonal cycle for flux tower GPP was not reproduced exactly and some overestimation in spring and underestimation in summer remained after calibration. We hypothesized that the phenology exhibited a seasonal cycle that was not accurately reproduced by the simulator. We investigated this by calibrating the Biome-BGC to each month's flux tower GPP separately. As expected, the simulated GPP improved, but the calibrated parameter values suggested that the seasonal cycle of state variables in the simulator could be improved. It was concluded that the Bayesian framework for calibration can reveal features of the modelled physical processes and identify aspects of the process simulator that are too rigid.


Raj, R., Tol, C. V. D., Hamm, N. A., & Stein, A. (2018). Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output. Geoscientific Model Development, 11(1),

Journal Article Type Article
Acceptance Date Nov 17, 2017
Publication Date Jan 9, 2018
Deposit Date Jul 19, 2018
Publicly Available Date Jul 19, 2018
Journal Geoscientific Model Development
Print ISSN 1991-959X
Electronic ISSN 1991-9603
Publisher European Geosciences Union
Peer Reviewed Peer Reviewed
Volume 11
Issue 1
Public URL
Publisher URL


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