Estimating random errors of eddy covariance data: An extended two-tower approach

Abstract

The incorporation of eddy covariance (EC) data in a land surface model (LSM) with help of data assimilation techniques requires a specification of the uncertainty of EC measurements. EC measurement uncertainty is composed of a systematic and random component. The systematic error is for example related to the energy balance (EB) closure, whereas the random error can be determined on the basis of differences between simultaneous flux measurements from two towers according to Hollinger and Richardson (2005) (Tree Physiol. 25, 873–885, here referred to as classical approach). The two-tower method, however, can be applied only where two towers share very similar environmental conditions. Here, we introduce an extended procedure to estimate the random error from EC data on the basis of the two-tower approach adapted for more heterogeneous environmental conditions. Our extended procedure consists of three main steps: (1) the EB deficit is corrected by distributing the deficit over the latent and sensible heat fluxes according to the evaporative fraction for each tower. This correction is based on the assumption that the EB deficit is due to an underestimation of the turbulent fluxes; (2) heterogeneity (e.g. different soil properties or vegetation characteristics and local variability in precipitation amounts) between two towers can introduce additional systematic flux differences. These differences can be corrected by normalizing turbulent fluxes at each tower according to the averaged evaporative fraction from two towers; (3) the random error can be determined following the two-tower approach using the normalized fluxes for the two towers. EC data from three different sites with different environmental conditions are used to test the classical and our extended approach: (1) three EC towers are placed at the Merken site, Germany and each of the three towers is surrounded by different vegetation types. This allows an evaluation on the basis of three different two-tower pairs. (2) two EC towers are located at the Roccarespampani site, Italy, with the same vegetation type around both towers. However, there are differences in vegetation age and density between these two towers; (3) for the Howland site, Maine, USA also data from two towers are available with very similar environmental conditions around the two towers. The random errors calculated by our extended approach are smaller than random errors from the classical approach, especially for larger net radiation (or large absolute fluxes). In addition, the random errors calculated by our extended approach result also in 9 out of 10 cases in less steep increases of the random error as function of flux magnitude (compared to the classical method). It was also found that atmospheric stability is an interesting alternative explanatory variable for random error of fluxes, which could be of special interest in the context of the extended two-tower approach. We conclude that our extended two-tower approach can be used to determine the random error of EC data for two towers located in more heterogeneous environmental conditions than aimed at by the original approach.