Uncertainty Analysis of Gross Primary Production Separated from Net Ecosystem Exchange Measurement at Speulderbos Forest, The Netherlands
Abstract:Gross primary production (GPP), separated from the flux tower measurements of net ecosystem exchange (NEE) of CO2, is used increasingly to validate process-based simulators and remote sensing-derived estimates of simulated GPP at various time scales. Proper implementation of validation requires knowledge of the uncertainty associated with the separated GPP at different time scales so that the error bar can be calculated. We estimate the uncertainty in GPP at half-hourly to yearly time scales. This is separated from the 3 years of continuous flux tower NEE measurements at the Speulderbos forest site, The Netherlands. We use a numerically efficient flux partitioning method (FPM) for GPP separation. FPM uses non-linear regression, based on the non-rectangular hyperbola equation. It was fitted to the measured NEE data on a daily basis. Parameters of the regression model were assumed to vary within a given year. FPM includes the factors influencing GPP, in particular radiation, vapor pressure deficit, degree of curvature of the light response curve and temperature. We implemented a Monte Carlo based bootstrap approach to estimate the uncertainty in the separated GPP at a half-hourly time scale. The expected results of this approach generated the empirical distribution of GPP at each half-hour. Together with its statistical properties, this provided estimates of uncertainty. The time series of empirical distributions of half-hourly GPP also allowed the estimation of the uncertainty at daily, monthly and yearly time scales.
Our research provided a robust integration of numerically efficient FPM and Monte Carlo approach to estimate uncertainty in GPP at different time scales. This will provide relevant and important information for the validation of process-based simulators. It will be applied at the Speulderbos forest site, The Netherlands.