Articles | Volume 13, issue 5
Biogeosciences, 13, 1409–1422, 2016
https://doi.org/10.5194/bg-13-1409-2016
Biogeosciences, 13, 1409–1422, 2016
https://doi.org/10.5194/bg-13-1409-2016

Research article 07 Mar 2016

Research article | 07 Mar 2016

Uncertainty analysis of gross primary production partitioned from net ecosystem exchange measurements

Rahul Raj, Nicholas Alexander Samuel Hamm, Christiaan van der Tol, and Alfred Stein Rahul Raj et al.
  • Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, the Netherlands

Abstract. Gross primary production (GPP) can be separated from flux tower measurements of net ecosystem exchange (NEE) of CO2. This is used increasingly to validate process-based simulators and remote-sensing-derived estimates of simulated GPP at various time steps. Proper validation includes the uncertainty associated with this separation. In this study, uncertainty assessment was done in a Bayesian framework. It was applied to data from the Speulderbos forest site, The Netherlands. We estimated the uncertainty in GPP at half-hourly time steps, using a non-rectangular hyperbola (NRH) model for its separation from the flux tower measurements. The NRH model provides a robust empirical relationship between radiation and GPP. It includes the degree of curvature of the light response curve, radiation and temperature. Parameters of the NRH model were fitted to the measured NEE data for every 10-day period during the growing season (April to October) in 2009. We defined the prior distribution of each NRH parameter and used Markov chain Monte Carlo (MCMC) simulation to estimate the uncertainty in the separated GPP from the posterior distribution at half-hourly time steps. This time series also allowed us to estimate the uncertainty at daily time steps. We compared the informative with the non-informative prior distributions of the NRH parameters and found that both choices produced similar posterior distributions of GPP. This will provide relevant and important information for the validation of process-based simulators in the future. Furthermore, the obtained posterior distributions of NEE and the NRH parameters are of interest for a range of applications.

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Short summary
We present a Bayesian estimation of uncertainty in half-hourly GPP partitioned from flux tower measurements of NEE. The results show that it is possible to do this at any desirable time step. This, in turn, can be used to quantify the propagated uncertainty when validating process-based simulators. We further show the importance of using non-informative priors compared to informative priors of the parameters of flux partitioning model as they speed up calculation without loss of precision.
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