On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm

Abstract

This paper discusses the advantages and disadvantages of the different methods that separate net ecosystem exchange (NEE) into its major components, gross ecosystem carbon uptake (GEP) and ecosystem respiration (R-eco). In particular, we analyse the effect of the extrapolation of night-time values of ecosystem respiration into the daytime; this is usually done with a temperature response function that is derived from long-term data sets. For this analysis, we used 16 one-year-long data sets of carbon dioxide exchange measurements from European and US-American eddy covariance networks. These sites span from the boreal to Mediterranean climates, and include deciduous and evergreen forest, scrubland and crop ecosystems. long-term (annual) data sets, does not reflect the short-term temperature sensitivity that is effective when extrapolating from night- to daytime. Specifically, in summer active ecosystems the long-term temperature sensitivity exceeds the short-term sensitivity. Thus, in those ecosystems, the application of a long-term temperature sensitivity to the extrapolation of respiration from night to day leads to a systematic overestimation of ecosystem respiration from half-hourly to annual time-scales, which can reach > 25% for an annual budget and which consequently affects estimates of GEP. Conversely, in summer passive (Mediterranean) ecosystems, the long-term temperature sensitivity is lower than the short-term temperature sensitivity resulting in underestimation of annual sums of respiration. temperature sensitivity of R-eco from eddy covariance data that applies this to the extrapolation from night- to daytime, and that further performs a filling of data gaps that exploits both, the covariance between fluxes and meteorological drivers and the temporal structure of the fluxes. While this algorithm should give less biased estimates of GEP and R-eco, we discuss the remaining biases and recommend that eddy covariance measurements are still backed by ancillary flux measurements that can reduce the uncertainties inherent in the eddy covariance data. [References: 53

Stand age and species richness dampen interannual variation of ecosystem-level photosynthetic capacity

“…From the global eddy covariance flux database 18 we downloaded half-hourly ecosystem-level gross primary productivity (GPP) fluxes estimated from net ecosystem exchange (NEE) data 34 . Half-hourly shortwave incoming global radiation, temperature, and precipitation were also downloaded.…”

Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

“…Level 4 eight-day data were used in this study to match the temporal resolution of the MODIS 8-day surface reflectance composite datasets. We used the gap-filled and partitioned GPP data from the Marginal Distribution Sampling (MDS) method (Reichstein et al, 2005). The meteorological data used in this study include air temperature, precipitation, vapor pressure deficit (VPD), and solar radiation.…”

“…Since the 1990s, the eddy covariance method has been used as an important tool to measure heat, water, and CO 2 exchanges as well as trace green-house gases (Baldocchi, 2014). The observed net ecosystem CO 2 exchange (NEE) at the ecosystem scale is partitioned into GPP and ecosystem respiration (R e , including both autotrophic and heterotrophic respiration components) (Desai et al, 2008;Papale et al, 2006;Reichstein et al, 2005). However, due to the limited number of flux tower sites and their footprints, estimation of GPP at the regional and global scales still relies on model simulation.…”

Improved data gap-filling schemes for estimation of net ecosystem exchange in typical East-Asian croplands

“…Assuming no photosynthesis during nighttime (Reichstein et al, 2005), the measured nighttime NEE is equal to R eco . Thus, the night-time NEE was used for the parameterization R eco (Section 2.3.2).…”

“…GPP was obtained by subtracting R eco from NEE and then used for evaluation of the gap-filling schemes. The separation of data into daytime data and nighttime was performed by the evaluation of global radiation with a threshold of 20 W m −2 and cross-checked against sunrise and sunset time derived from the local time and standard sun-geometrical routines (Reichstein et al, 2005;Papale et al, 2006;Lasslop et al, 2010).…”