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

Abstract

Parameters of a process-based forest growth simulator are difficult or impossible to obtain from field observations. Reliable estimates can, however, 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 5 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 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 soil rooting depth (SD) characterize the photosynthesis and carbon and nitrogen allocation in the 10 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 overestimate in spring and underestimates in summer remained after calibration. Further analysis showed that, although simulated GPP was time dependent due to carbon allocation, it still followed the variability of the meteorological forcing closely. We hypothesized that the phenology exhibited a seasonal cycle that was not accurately 15 reproduced by the simulator. We investigated this by allowing the parameter values to vary month-by-month.