Does predictability of fluxes vary between FLUXNET sites?

Ned Haughton*, Gab Abramowitz, Martin G. De Kauwe, Andy J. Pitman

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

9 Citations (Scopus)

Abstract

The FLUXNET dataset contains eddy covariance measurements from across the globe and represents an invaluable estimate of the fluxes of energy, water, and carbon between the land surface and the atmosphere. While there is an expectation that the broad range of site characteristics in FLUXNET result in a diversity of flux behaviour, there has been little exploration of how predictable site behaviour is across the network. Here, 155 datasets with 30 min temporal resolution from the Tier 1 of FLUXNET 2015 were analysed in a first attempt to assess individual site predictability. We defined site uniqueness as the disparity in performance between multiple empirical models trained globally and locally for each site and used this along with the mean performance as measures of predictability. We then tested how strongly uniqueness was determined by various site characteristics, including climatology, vegetation type, and data quality. The strongest determinant of predictability appeared to be that drier sites tended to be more unique. We found very few other clear predictors of uniqueness across different sites, in particular little evidence that flux behaviour was well discretised by vegetation type. Data length and quality also appeared to have little impact on uniqueness. While this result might relate to our definition of uniqueness, we argue that our approach provides a useful basis for site selection in LSM evaluation, and we invite critique and development of the methodology.

Original languageEnglish
Pages (from-to)4495-4513
Number of pages19
JournalBiogeosciences
Volume15
Issue number14
DOIs
Publication statusPublished - 25 Jul 2018

Bibliographical note

Funding Information:
Acknowledgements. We acknowledge the support of the Australian Research Council Centre of Excellence for Climate System Science (CE110001028) and the ARC Centre of Excellence for Climate Extremes (CE170100023). This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The ERA-Interim reanalysis data are provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonisation was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and Fluxdata project of FLUXNET with the support of CDIAC and ICOS Ecosystem Thematic Center, as well as the OzFlux, ChinaFlux, and AsiaFlux offices. A full list of FLUXNET sites and citations is included in the Supplement. We would also like to thank two anonymous reviewers for insightful comments that helped improve the paper.

Publisher Copyright:
© Author(s) 2018.

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