Fostering collaboration through improved software development practices for the ONEFlux eddy covariance data processing pipeline

Abstract

<p>Standardized processing of eddy covariance data is important for studies combining data from multiple sites, for validating remote sensing measurements as well as runs of ecosystem and climate models, and for applications relying on these flux data to create derived products like upscaled fluxes, among other examples. However, maintaining consistency within the software used for this processing while allowing for evolution of this code across research networks presents novel challenges in software development. The introduction of the ONEFlux (Open Network-Enabled Flux) eddy covariance data processing pipeline, originally developed within a collaboration of the AmeriFlux Management Project, the European Fluxes Database, and the ICOS Ecosystem Thematic Centre, supported the creation of consistently processed global eddy covariance data products. In particular, ONEFlux codes were used to generate the FLUXNET2015 dataset, which is widely adopted by thousands of eddy covariance data users in their work in research, ranging from soil microbiology to large scale drought effects, and also education, from basic plant biology all the way to global climate change. We are now more thoroughly instrumenting the code, and the code development process, to better address these challenges, efforts which we will describe in this presentation. In particular, we are seeking to improve software development practices to allow for more streamlined collaboration on expanding and contributing to the codebase. For instance, we are adopting planned release cycles for code updates, designing more detailed ways to incorporate and evaluate new modules, introducing data-centric testing and continuous integration, improving code performance, and adopting several other software engineering best practices more widely in the development workflows. The main goal of these changes is to lower the barriers for running ONEFlux by regional networks processing their data, while at the same time better supporting contributions from the community into the codebase. This will be critical to continue the current use of ONEFlux to generate updated versions of flux datasets by regional networks, the components of new global products.</p>

Quantifying effects of cold acclimation and delayed springtime photosynthesis resumption in northern ecosystems

“…To investigate DSPR, we selected 48 forest sites for which eddy covariance (EC) measurements of ecosystem CO 2 exchange and simultaneous meteorological variables were available from the FLUXNET 2015 datasets (Pastorello et al, 2020). Sites were…”

Contrasting responses of peak vegetation growth to asymmetric warming: Evidences from FLUXNET and satellite observations

“…CO 2 flux data of all study sites were derived from the FLUXNET2015 Data set (https://fluxn et.org/data/fluxn et201 5-datas et/). Several improvements were used to control data quality, improve consistency and inter-comparability across flux sites (Pastorello et al, 2020).…”

Evaluation of leaf phenology of different vegetation types from local to hemispheric scale in CLM

“…We also calculated how large ∆GPP is compared to the total annual GPP in CLM and named the index ∆GPP pheno . To verify the inferred GPP and LAI seasonal cycles, we adopted site-level LAI and GPP estimates from two AmeriFlux flux tower sites (Pastorello et al, 2020)…”