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Abstract

Biomass as a resource, and as a vulnerable carbon pool, is a key variable to diagnose the impacts of global changes on the terrestrial biosphere, and therefore its proper description in models is crucial. Model-Data Fusion (MDF) or data assimilation methods are useful tools in improving ecosystem models that describe interactions between vegetation and atmosphere. We use a MDF method based on a Bayesian approach, in which data are combined with a process model in order to provide optimized estimates of model parameters and to better quantify model uncertainties, whilst taking into account prior information on the parameters. With this method we are able to use multiple data streams, which allows us to simultaneously constrain modeled variables at site level across different temporal scales. In this study both high frequency eddy covariance flux measurements of net CO2 and evapotranspiration (ET), and low frequency biometric measurements of total aboveground biomass and the annual increment (which includes all compartments), are assimilated with the ORCHIDEE model version “AR5” at a beech (Hesse) and a maritime pine (Le Bray) forest site using four to five years of flux data and nine years of biomass data. When assimilating the observed aboveground annual biomass increment (AGB_inc) together with net CO2 and ET flux, the RMSE of modelled AGB_inc was reduced from the a priori estimates by 37% at Hesse and 69% at Le Bray, without reducing the fit to the net CO2 and ET that can be achieved when assimilating flux data alone. Assimilating biomass increment data also provides insight in the performance of the allocation scheme of the model. Comparison with detailed site-based measurements at Hesse showed that the optimization reduced positive biases in the model, for example in fine root and leaf production. We also investigated how to use stand-scale total aboveground biomass in optimization (AGB_tot). However, this study demonstrated that assimilating AGB_tot measurements in the ORCHIDEE-AR5 model lead to some inconsistencies, particularly for the annual dynamics of the AGB_inc, partly because this version of the model lacked a realistic representation of forest stand processes including management and disturbances.