Optimizing a process‐based ecosystem model with eddy‐covariance flux measurements: A pine forest in southern France

Abstract

[1] We design a Bayesian inversion method (gradient-based) to optimize the key functioning parameters of a process-driven land surface model (ORganizing Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE)) against the combination of prior information upon the parameters and eddy covariance fluxes. The model calculates energy, water, and CO 2 fluxes and their interactions on a half-hourly basis, and we carry out the inversion using measurements of CO 2 , latent heat, and sensible heat fluxes as well as of net radiation over a pine forest in southern France. The inversion method makes it possible to assess the reduction of uncertainties and error correlations of the parameters. We designed an ensemble of inversions with different set ups using flux data over different time periods, in order to (1) identify well-constrained parameters and loosely constrained ones, (2) highlight some model structural deficiencies, and (3) quantify the overall information gained from assimilating each type of CO 2 or energy fluxes. The sensitivity of the optimal parameter values to the initial carbon pool sizes and prior parameter values is discussed and an analysis of the posterior uncertainties is performed. Assimilating 3 weeks of half-hourly flux data during the summer improves the fit to diurnal variations, but merely improves the fit to seasonal variations. Assimilating a full year of flux data also improves the fit to the diurnal cycle more than to the seasonal cycle. This points out to the key importance of timescales when inverting parameters from high-frequency eddy-covariance data. We show that photosynthetic parameters such as carboxylation rates are well-constrained by the carbon and water fluxes data and get increased from their prior values, a correction that is corroborated by independent measurements at leaf scale. In contrast, the parameters controlling maintenance, microbial and growth respirations, and their temperature dependencies cannot be robustly determined. The CO 2 flux data could not discriminate between the different respiration terms. At face value, all the parameters controlling the surface energy budget can be safely determined, leading to a good model-data fit on different timescales.Citation: Santaren, D., P. Peylin, N. Viovy, and P. Ciais (2007), Optimizing a process-based ecosystem model with eddy-covariance flux measurements: A pine forest in southern France, Global Biogeochem. Cycles, 21, GB2013,

Towards a representation of priming on soil carbon decomposition in the global land biosphere model ORCHIDEE (version 1.9.5.2)

“…The time step of the model is in half-hourly, and the variations of H 2 O and C pools are calculated on a daily basis. The model has been evaluated at different scales (sites, regions, globes) and under different climates from the tropics to northern boreal zones Ciais et al, 2005;Santaren et al, 2007;Piao et al, 2006). ORCHIDEE results from the coupling of three different sub-models.…”

“…(6) and (7) because the range of observations was quite limited. Optimization was performed in the framework of the Bayesian inversion method with priors (Tarantola, 1987) as described by Santaren et al (2007) assimilating all data streams in the same cost function. Assuming that all uncertainties follow Gaussian distributions (parameter error, measurement error, model error), the optimized parameters correspond to a set minimizing the following quadratic cost function:…”

High organic inputs explain shallow and deep SOC storage in a long-term agroforestry system – combining experimental and modeling approaches

“…As the BFGS algorithm does not directly calculate the variance of posteriors, they were quantified using the curvature cost function at its minimum once it was reached (Santaren et al, 2007).…”

“…Depending on the model variant, four to five parameters were optimized with a gradient-based statistical method (Santaren et al, 2007;Tarantola, 1987Tarantola, , 2005 using measured SOC stocks from the control plot only. These parameters were A the advection rate, D the diffusion coefficient, h the humification yield, a the coefficient of the k HSOC rate from Eq.…”

Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

“…The energy balance takes into account the skin temperature as presented in Schulz et al (2001) to derive the land-surface temperature (LST). The soil and vegetation are considered as a single medium assigned with a surface temperature (Santaren et al, 2007). …”