Technical note: Dynamic INtegrated Gap-filling and partitioning for OzFlux (DINGO)
- 1School of Earth and Environment (SEE), The University of Western Australia, Crawley WA, 6009, Australia
- 2School of Earth, Atmosphere and Environment, Monash University, Clayton, 3800, Australia
- 3School of Environment, Research Institute for the Environment and Livelihoods, Charles Darwin University, NT 0909, Australia
- 4School of Earth, Atmosphere and Environment, Monash University, Clayton, 3800, Australia
- 5Department of Geography, Swansea University, Singleton Park, Swansea, Wales SA2 8PP, UK
Abstract. Standardised, quality-controlled and robust data from flux networks underpin the understanding of ecosystem processes and tools necessary to support the management of natural resources, including water, carbon and nutrients for environmental and production benefits. The Australian regional flux network (OzFlux) currently has 23 active sites and aims to provide a continental-scale national research facility to monitor and assess Australia's terrestrial biosphere and climate for improved predictions. Given the need for standardised and effective data processing of flux data, we have developed a software suite, called the Dynamic INtegrated Gap-filling and partitioning for OzFlux (DINGO), that enables gap-filling and partitioning of the primary fluxes into ecosystem respiration (Fre) and gross primary productivity (GPP) and subsequently provides diagnostics and results. We outline the processing pathways and methodologies that are applied in DINGO (v13) to OzFlux data, including (1) gap-filling of meteorological and other drivers; (2) gap-filling of fluxes using artificial neural networks; (3) the u* threshold determination; (4) partitioning into ecosystem respiration and gross primary productivity; (5) random, model and u* uncertainties; and (6) diagnostic, footprint calculation, summary and results outputs. DINGO was developed for Australian data, but the framework is applicable to any flux data or regional network. Quality data from robust systems like DINGO ensure the utility and uptake of the flux data and facilitates synergies between flux, remote sensing and modelling.