Abstract
Abstract. 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.
Highlights
Forest ecosystems play an important role in the global carbon cycle by controlling the atmospheric CO2 level
Use of the framework led to the following conclusions: 1. The Bayesian framework allowed quantification of uncertainty in both the estimated parameters and the posterior gross primary production (GPP), through the posterior distribution
The uncertainty is important in the sense that it helps to determine how much confidence can be placed in the results of forest carbon-related studies based on GPP
Summary
Forest ecosystems play an important role in the global carbon cycle by controlling the atmospheric CO2 level. Knowledge of gross primary production (GPP) for forest ecosystems is indispensable for the estimation of forest carbon storage. GPP is the first entry of atmospheric carbon into the forest ecosystem via photosynthesis. Process-based forest simulators (PBSs) evaluate forest ecosystem activity by simulating different physiological plant responses to climatic conditions, atmospheric properties and plant structures (Constable and Friend, 2000; Running, 1994). Simulating a PBS requires input parameters that distinguish different vegetation types by their physiological and morphological characteristics. Implementation of a PBS for specific sites is complicated by the large number of parameters for plants, the soil and the atmosphere. Field measurements of PBS parameters are difficult or impossible to obtain, leading to incomplete knowledge of site-specific parameters for the occurring species. Practitioners often rely on the literature for values of the PBS parameters (Hartig et al, 2012; Mäkelä et al, 2000)
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