Volume 15, issue 14 | Copyright
Biogeosciences, 15, 4495-4513, 2018
https://doi.org/10.5194/bg-15-4495-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 25 Jul 2018

Research article | 25 Jul 2018

Does predictability of fluxes vary between FLUXNET sites?

Ned Haughton, Gab Abramowitz, Martin G. De Kauwe, and Andy J. Pitman Ned Haughton et al.
  • Climate Change Research Centre, UNSW, Sydney, Australia

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 30min 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.

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This project explores predictability in energy, water, and carbon fluxes in the free-use Tier 1 of the FLUXNET 2015 dataset using a uniqueness metric based on comparison of locally and globally trained models. While there is broad spread in predictability between sites, we found strikingly few strong patterns. Nevertheless, these results can contribute to the standardisation of site selection for land surface model evaluation and help pinpoint regions that are ripe for further FLUXNET research.
This project explores predictability in energy, water, and carbon fluxes in the free-use Tier 1...
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