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

Abstract

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.

Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems

“…the error variance varies with the magnitude of the flux. In a more recent study Raj et al (2016) investigated the uncertainty of GPP derived from partitioning the eddy covariance NEP measurements. They used a light-use efficiency model embedded in a Bayesian framework to estimate the uncertainty in the separated GPP from the posterior distribution at half-hourly time steps.…”

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

“…Biome-BGC simulates biogeochemical processes including carbon, water, and nitrogen fluxes within the vegetation, litter, and soil compartment of terrestrial ecosystem at daily time steps Running and Hunt, 1993). Evapotranspiration (ET), photosynthesis, and respiration (autotrophic and heterotrophic) are the main processes simulated by Biome-BGC.…”

“…Hence, the likelihood function evaluates how well the Biome-BGC simulated GPP, y, is able to reproduce the data, z. The likelihood function is typically defined assuming that the residuals are independent and identically normally distributed (Starrfelt and Kaste, 2014;Braakhekke et al, 2013;Reinds et al, 2008;Svensson et al, 2008;van Oijen et al, 2005). This assumes that the simulator models perfectly the temporal profile of GPP leaving no residual temporal correlation in the residuals from the time series.…”

“…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).…”

“…A Bayesian framework provides a coherent method for calibrating a PBS (van Oijen et al, 2011;Reinds et al, 2008;van Oijen et al, 2005) and involves the identification of uncertainty in the parameters from the available information. This uncertainty is expressed as the prior probability distributions of the parameters.…”