Assimilating and reporting uncertainty in eddy covariance data products across the NEON network

Background/Question/Methods

One goal of the National Ecological Observatory Network (NEON) is to provide high quality ecosystem exchange observations to the science community. The eddy covariance terrestrial exchange subsystem (EC-TES) will conduct measurements of turbulent exchange of sensible heat, water vapor, and CO₂ at 60 terrestrial sites across the network. The theoretical assumptions underlying the EC method cannot always be met, e.g. for research sites located in topographically challenging terrain or heterogeneous land cover. Even though there are no ideal sites, in practice the EC method has still proven to provide robust results under most micrometeorological conditions. The eddy covariance data are useful in a variety of ways, e.g., constructing long times series for annual carbon balance. However, long time series will inevitably involve the removal of suspect data and the gap-filling of those data, which is also associated with some uncertainty. There are even certain limitations associated with the measurement instrumentation and setup that must accounted for through empirically derived corrections that result in some uncertainty. To allow scientists and policy makers to answer their scientific questions with a precise degree of certainty in the results, a quality control and quality assurance (QA/QC) framework that provides the needed granularity in quality metrics to satisfy a wide range of questions is needed. This study addresses how NEON will report uncertainty in its eddy covariance data products through a transparent and transferable QA/QC framework.

Results/Conclusions

The synthesis and reporting of the uncertainty associated with the instrumentation, the measurement site, and algorithmic implementation is essential to providing the scientific community with reliable data that is representative of the various ecosystems measured by the NEON network. A framework was developed to objectively assess data quality, while remaining transparent and transferable to all of NEON’s terrestrial sensors (Smith et al., 2014). However, this framework has been used primarily for simpler measurement systems until now. This study utilizes this framework to assess a much more complex measurement platform with many associated sources of uncertainty. The sources of uncertainty are outlined for eddy covariance data products and reported in various granularities allowing data users to understand fully the uncertainty associated with the NEON eddy covariance data products.