Modern advances in biometeorological monitoring technology have improved the capacity for measuring ecosystem exchanges of mass, energy and scalars (such as CO2). Translating these measurements into robust and accurate scientific information (and ultimately, understanding) requires careful assessment of operations throughout the biometeorological data life cycle. In response, this research analyzed and optimized aspects of data collection, management and filtering for an ecosystem exchange measurement program over an age-sequence of temperate white pine forests.
A comprehensive data workflow and management system (DWMS) was developed and implemented to support the entire data life cycle for all past, present and future measurement operations in our research group, and meet the needs of a collaborative, student-led data management environment. Best practices for biometeorological data management were introduced and used as standards to assess system performance.
Roving eddy covariance (rEC) systems were examined as a means of producing reliable time-integrated carbon exchange estimates at multiple sites, by rotating an EC system in a resource-mindful approach. When used with an optimal gap-filling model and rEC rotation schedule (2 sites with 15-day rotations), the results suggested its viability, as annual NEE estimate uncertainties ranged between 35 and 63% of the annual NEE flux magnitude at our study sites – even though approximately 70% of half-hours were filled.
Lastly, a data-driven approach was used to investigate the effects of different friction velocity and footprint filtering methods on time-integrated carbon exchange estimates at our fetch-limited forests. Though predicted flux source areas varied considerably between footprint models, our objective analyses identified the model (Kljun et al., 2004) and within-fetch requirement (80%) that optimized reliability and representativeness of carbon exchange estimates. Applying this footprint model decreased annual NEE by 31 to 129% (59 to 207 g C m-2 y-1) relative to no footprint application, and highlighted the importance of objective analyses of EC flux filtering methods.