AmeriFlux datasets provide the crucial linkage between organisms, ecosystems, and process-scale studies at climate-relevant scales of landscapes, regions, and continents, which can be incorporated into biogeochemical and climate models. When viewed as a whole, the network observations enable scaling of trace gas fluxes (CO2, water vapor) across a broad spectrum of times (hours, days, seasons, years, and decades) and space. AmeriFlux observations have been instrumental in defining the relationships between environmental drivers and responses of whole ecosystems, which can be spatialized using machine learning methods like neural networks or genetic algorithms informed by remote sensing products. The AmeriFlux Network Management Project will fund core AmeriFlux sites and will establish data management, and data QA/QC processes for those sites.
Researchers in the Integrated Data Frameworks group have been core contributors to the data management aspects for the AmeriFlux Network:
- Investigation and implementation of methods for data integration at multiple stages of processing
- Data quality assurance for heterogeneous data sources
- Algorithms for execution and evaluation of data processing pipelines
- Data models and data behavior models for observational data, which allowed a more complete characterization of data quality and improved evaluation of data processing algorithms.