An evaluation of models for partitioning eddy covariance-measured net ecosystem exchange into photosynthesis and respiration

We measured net ecosystem CO2 exchange (NEE) using the eddy covariance (EC) technique for 4 years at adjoining old field (OF), planted pine (PP) and hardwood forest (HW) ecosystems in the Duke Forest, NC. To compute annual sums of NEE and its components – gross ecosystem productivity (GEP) and ecosystem respiration (RE) – different ‘flux partitioning’ models (FPMs) were tested and the resulting C flux estimates were compared against published estimates from C budgeting approaches, inverse models, physiology-based forward models, chamber respiration measurements, and constraints on assimilation based on sapflux and evapotranspiration measurements. Our analyses demonstrate that the more complex FPMs, particularly the ‘non-rectangular hyperbolic method’, consistently produced the most reasonable C flux estimates. Of the FPMs that use nighttime data to estimate RE, one that parameterized an exponential model over short time periods generated predictions that were closer to expected flux values. To explore how much ‘new information’ was injected into the data by the FPMs, we used formal information theory methods and computed the Shannon entropy for: (1) the probability density, to assess alterations to the flux measurement distributions, and (2) the wavelet energy spectra, to assess alterations to the internal autocorrelation within the NEE time series. Based on this joint analysis, gap-filling had little impact on the IC of daytime data, but gap-filling significantly altered nighttime data in both the probability and wavelet spectral domains.