Articles | Volume 10, issue 1
https://doi.org/10.5194/gi-10-123-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gi-10-123-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Jun 2021
Research article |
| 28 Jun 2021
| 28 Jun 2021
A comparison of gap-filling algorithms for eddy covariance fluxes and their drivers
School of Agriculture and Environment, The University of Western
Australia, 35 Stirling Hwy, Crawley, Perth, WA, 6009, Australia
Jason Beringer
School of Agriculture and Environment, The University of Western
Australia, 35 Stirling Hwy, Crawley, Perth, WA, 6009, Australia
Matthias Leopold
School of Agriculture and Environment, The University of Western
Australia, 35 Stirling Hwy, Crawley, Perth, WA, 6009, Australia
Ian McHugh
School of Ecosystem and Forest Sciences, The University of
Melbourne, Richmond, VIC, 3121, Australia
James Cleverly
School of Life Sciences University of Technology Sydney Broadway
Sydney, NSW, 2007, Australia
Peter Isaac
OzFlux Central Node, TERN Ecosystem Processes, Melbourne, VIC, 3159, Australia
Azizallah Izady
Water Research Center, Sultan Qaboos University, Muscat, Oman
Related authors
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Bimal K. Bhattacharya, Kaniska Mallick, Devansh Desai, Ganapati S. Bhat, Ross Morrison, Jamie R. Clevery, William Woodgate, Jason Beringer, Kerry Cawse-Nicholson, Siyan Ma, Joseph Verfaillie, and Dennis Baldocchi
Biogeosciences, 19, 5521–5551, https://doi.org/10.5194/bg-19-5521-2022, https://doi.org/10.5194/bg-19-5521-2022, 2022
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Evaporation retrieval in heterogeneous ecosystems is challenging due to empirical estimation of ground heat flux and complex parameterizations of conductances. We developed a parameter-sparse coupled ground heat flux-evaporation model and tested it across different limits of water stress and vegetation fraction in the Northern/Southern Hemisphere. The model performed particularly well in the savannas and showed good potential for evaporative stress monitoring from thermal infrared satellites.
Remko C. Nijzink, Jason Beringer, Lindsay B. Hutley, and Stanislaus J. Schymanski
Geosci. Model Dev., 15, 883–900, https://doi.org/10.5194/gmd-15-883-2022, https://doi.org/10.5194/gmd-15-883-2022, 2022
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The Vegetation Optimality Model (VOM) is a coupled water–vegetation model that predicts vegetation properties rather than determines them based on observations. A range of updates to previous applications of the VOM has been made for increased generality and improved comparability with conventional models. This showed that there is a large effect on the simulated water and carbon fluxes caused by the assumption of deep groundwater tables and updated soil profiles in the model.
Remko C. Nijzink, Jason Beringer, Lindsay B. Hutley, and Stanislaus J. Schymanski
Hydrol. Earth Syst. Sci., 26, 525–550, https://doi.org/10.5194/hess-26-525-2022, https://doi.org/10.5194/hess-26-525-2022, 2022
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Most models that simulate water and carbon exchanges with the atmosphere rely on information about vegetation, but optimality models predict vegetation properties based on general principles. Here, we use the Vegetation Optimality Model (VOM) to predict vegetation behaviour at five savanna sites. The VOM overpredicted vegetation cover and carbon uptake during the wet seasons but also performed similarly to conventional models, showing that vegetation optimality is a promising approach.
Sophie V. J. van der Horst, Andrew J. Pitman, Martin G. De Kauwe, Anna Ukkola, Gab Abramowitz, and Peter Isaac
Biogeosciences, 16, 1829–1844, https://doi.org/10.5194/bg-16-1829-2019, https://doi.org/10.5194/bg-16-1829-2019, 2019
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Measurements of surface fluxes are taken around the world and are extremely valuable for understanding how the land and atmopshere interact, and how the land can amplify temerature extremes. However, do these measurements sample extreme temperatures, or are they biased to the average? We examine this question and highlight data that do measure surface fluxes under extreme conditions. This provides a way forward to help model developers improve their models.
Rizwana Rumman, James Cleverly, Rachael H. Nolan, Tonantzin Tarin, and Derek Eamus
Hydrol. Earth Syst. Sci., 22, 4875–4889, https://doi.org/10.5194/hess-22-4875-2018, https://doi.org/10.5194/hess-22-4875-2018, 2018
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Groundwater is a significant water resource for humans and for groundwater-dependent vegetation. Several challenges to managing both groundwater resources and dependent vegetation include defining the location of dependent vegetation, the rate of groundwater use, and the depth of roots accessing groundwater. In this study we demonstrate a novel application of measurements of stable isotopes of carbon that can be used to identify the location, the rooting depth, and the rate of groundwater use.
Alexandre A. Renchon, Anne Griebel, Daniel Metzen, Christopher A. Williams, Belinda Medlyn, Remko A. Duursma, Craig V. M. Barton, Chelsea Maier, Matthias M. Boer, Peter Isaac, David Tissue, Victor Resco de Dios, and Elise Pendall
Biogeosciences, 15, 3703–3716, https://doi.org/10.5194/bg-15-3703-2018, https://doi.org/10.5194/bg-15-3703-2018, 2018
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We report the seasonality of net ecosystem–atmosphere CO2 exchange (NEE) in a temperate evergreen broadleaved forest in Sydney, Australia. We investigated how carbon exchange varied with climatic drivers and canopy dynamics (leaf area index, litter fall). We found that our site acted as a net source of carbon in summer and a net sink in winter. Ecosystem respiration (ER) drove NEE seasonality, as the seasonal amplitude of ER was greater than gross primary productivity.
Eva van Gorsel, James Cleverly, Jason Beringer, Helen Cleugh, Derek Eamus, Lindsay B. Hutley, Peter Isaac, and Suzanne Prober
Biogeosciences, 15, 349–352, https://doi.org/10.5194/bg-15-349-2018, https://doi.org/10.5194/bg-15-349-2018, 2018
Rhys Whitley, Jason Beringer, Lindsay B. Hutley, Gabriel Abramowitz, Martin G. De Kauwe, Bradley Evans, Vanessa Haverd, Longhui Li, Caitlin Moore, Youngryel Ryu, Simon Scheiter, Stanislaus J. Schymanski, Benjamin Smith, Ying-Ping Wang, Mathew Williams, and Qiang Yu
Biogeosciences, 14, 4711–4732, https://doi.org/10.5194/bg-14-4711-2017, https://doi.org/10.5194/bg-14-4711-2017, 2017
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This paper attempts to review some of the current challenges faced by the modelling community in simulating the behaviour of savanna ecosystems. We provide a particular focus on three dynamic processes (phenology, root-water access, and fire) that are characteristic of savannas, which we believe are not adequately represented in current-generation terrestrial biosphere models. We highlight reasons for these misrepresentations, possible solutions and a future direction for research in this area.
Nina Hinko-Najera, Peter Isaac, Jason Beringer, Eva van Gorsel, Cacilia Ewenz, Ian McHugh, Jean-François Exbrayat, Stephen J. Livesley, and Stefan K. Arndt
Biogeosciences, 14, 3781–3800, https://doi.org/10.5194/bg-14-3781-2017, https://doi.org/10.5194/bg-14-3781-2017, 2017
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We undertook a 3-year study (2010–2012) of eddy covariance measurements in a dry temperate eucalypt (broadleaf evergreen) forest in southeastern Australia. The forest was a large and constant carbon sink, with the greatest uptake in early spring and summer. A strong seasonal pattern in environmental controls of daytime and night-time NEE was revealed. Our results show the potential of temperate eucalypt forests to sequester large amounts of carbon when not water limited.
Ian D. McHugh, Jason Beringer, Shaun C. Cunningham, Patrick J. Baker, Timothy R. Cavagnaro, Ralph Mac Nally, and Ross M. Thompson
Biogeosciences, 14, 3027–3050, https://doi.org/10.5194/bg-14-3027-2017, https://doi.org/10.5194/bg-14-3027-2017, 2017
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We analysed a 3-year record of CO2 exchange at a eucalypt woodland and found that substantial nocturnal advective CO2 losses occurred, thus requiring correction. We demonstrated that the most common of these correction methods incurred substantial bias in long-term estimates of carbon balance if storage of CO2 below the measurement height was excluded. This is important because the majority of sites both in Australia and internationally lack such measurements.
Peter Isaac, James Cleverly, Ian McHugh, Eva van Gorsel, Cacilia Ewenz, and Jason Beringer
Biogeosciences, 14, 2903–2928, https://doi.org/10.5194/bg-14-2903-2017, https://doi.org/10.5194/bg-14-2903-2017, 2017
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Networks of flux towers present diverse challenges to data collectors, managers and users. For data collectors, the goal is to minimise the time spent producing usable data sets. For data managers, the challenge is making data available in a timely and broad manner. For data users, the quest is for consistency in data processing across sites and networks. The OzFlux data path was developed to address these disparate needs and serves as an example of intra- and inter-network integration.
Jason Beringer, Ian McHugh, Lindsay B. Hutley, Peter Isaac, and Natascha Kljun
Biogeosciences, 14, 1457–1460, https://doi.org/10.5194/bg-14-1457-2017, https://doi.org/10.5194/bg-14-1457-2017, 2017
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Standardised, quality-controlled and robust data from flux networks underpin the understanding of ecosystem processes and tools to manage our natural resources. The Dynamic INtegrated Gap-filling and partitioning for OzFlux (DINGO) system enables gap-filling and partitioning of fluxes and subsequently provides diagnostics and results. Quality data from robust systems like DINGO ensure the utility and uptake of flux data and facilitates synergies between flux, remote sensing and modelling.
Cassandra Denise Wilks Rogers and Jason Beringer
Biogeosciences, 14, 597–615, https://doi.org/10.5194/bg-14-597-2017, https://doi.org/10.5194/bg-14-597-2017, 2017
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Savannas are extensive yet sensitive to variability in precipitation. We examined the relationship between climate phenomena and historical rainfall variability across Australian savannas using 16 climate indicies. Seasonal variation was most correlated with the Australian Monsoon Index, whereas interannual variability was related to a greater number of phenomena. Rainfall variability and the underlying climate processes driving variability are important.
Caitlin E. Moore, Jason Beringer, Bradley Evans, Lindsay B. Hutley, and Nigel J. Tapper
Biogeosciences, 14, 111–129, https://doi.org/10.5194/bg-14-111-2017, https://doi.org/10.5194/bg-14-111-2017, 2017
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Separating tree and grass productivity dynamics in savanna ecosystems is vital for understanding how they function over time. We showed how tree-grass phenology information can improve model estimates of gross primary productivity in an Australian tropical savanna. Our findings will contribute towards improved modelling of productivity in savannas, which will assist with their management into the future.
Mila Bristow, Lindsay B. Hutley, Jason Beringer, Stephen J. Livesley, Andrew C. Edwards, and Stefan K. Arndt
Biogeosciences, 13, 6285–6303, https://doi.org/10.5194/bg-13-6285-2016, https://doi.org/10.5194/bg-13-6285-2016, 2016
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Northern Australian savanna landscapes are a region earmarked for potential agricultural expansion. Greenhouse gas emissions from savanna land use change were quantified to determine the relative impact of increased rates of deforestation on Australia's national greenhouse gas accounts. Emissions from historic rates of deforestation were similar to savanna burning, but expanded clearing across northern Australia could add 3 % to Australia’s national greenhouse gas emissions.
Eva van Gorsel, Sebastian Wolf, James Cleverly, Peter Isaac, Vanessa Haverd, Cäcilia Ewenz, Stefan Arndt, Jason Beringer, Víctor Resco de Dios, Bradley J. Evans, Anne Griebel, Lindsay B. Hutley, Trevor Keenan, Natascha Kljun, Craig Macfarlane, Wayne S. Meyer, Ian McHugh, Elise Pendall, Suzanne M. Prober, and Richard Silberstein
Biogeosciences, 13, 5947–5964, https://doi.org/10.5194/bg-13-5947-2016, https://doi.org/10.5194/bg-13-5947-2016, 2016
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Temperature extremes are expected to become more prevalent in the future and understanding ecosystem response is crucial. We synthesised measurements and model results to investigate the effect of a summer heat wave on carbon and water exchange across three biogeographic regions in southern Australia. Forests proved relatively resilient to short-term heat extremes but the response of woodlands indicates that the carbon sinks of large areas of Australia may not be sustainable in a future climate.
Jason Beringer, Lindsay B. Hutley, Ian McHugh, Stefan K. Arndt, David Campbell, Helen A. Cleugh, James Cleverly, Víctor Resco de Dios, Derek Eamus, Bradley Evans, Cacilia Ewenz, Peter Grace, Anne Griebel, Vanessa Haverd, Nina Hinko-Najera, Alfredo Huete, Peter Isaac, Kasturi Kanniah, Ray Leuning, Michael J. Liddell, Craig Macfarlane, Wayne Meyer, Caitlin Moore, Elise Pendall, Alison Phillips, Rebecca L. Phillips, Suzanne M. Prober, Natalia Restrepo-Coupe, Susanna Rutledge, Ivan Schroder, Richard Silberstein, Patricia Southall, Mei Sun Yee, Nigel J. Tapper, Eva van Gorsel, Camilla Vote, Jeff Walker, and Tim Wardlaw
Biogeosciences, 13, 5895–5916, https://doi.org/10.5194/bg-13-5895-2016, https://doi.org/10.5194/bg-13-5895-2016, 2016
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OzFlux is the regional Australian and New Zealand flux tower network that aims to provide a continental-scale national facility to monitor and assess trends, and improve predictions, of Australia’s terrestrial biosphere and climate. We describe the evolution, design, and status as well as an overview of data processing. We suggest that a synergistic approach is required to address all of the spatial, ecological, human, and cultural challenges of managing Australian ecosystems.
Natalia Restrepo-Coupe, Alfredo Huete, Kevin Davies, James Cleverly, Jason Beringer, Derek Eamus, Eva van Gorsel, Lindsay B. Hutley, and Wayne S. Meyer
Biogeosciences, 13, 5587–5608, https://doi.org/10.5194/bg-13-5587-2016, https://doi.org/10.5194/bg-13-5587-2016, 2016
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We re-evaluated the connection between satellite greenness products and C-flux tower data in four Australian ecosystems. We identify key mechanisms driving the carbon cycle, and provide an ecological basis for the interpretation of vegetation indices. We found relationships between productivity and greenness to be non-significant in meteorologically driven evergreen forests and sites where climate and vegetation phenology were asynchronous, and highly correlated in phenology-driven ecosystems.
Caitlin E. Moore, Tim Brown, Trevor F. Keenan, Remko A. Duursma, Albert I. J. M. van Dijk, Jason Beringer, Darius Culvenor, Bradley Evans, Alfredo Huete, Lindsay B. Hutley, Stefan Maier, Natalia Restrepo-Coupe, Oliver Sonnentag, Alison Specht, Jeffrey R. Taylor, Eva van Gorsel, and Michael J. Liddell
Biogeosciences, 13, 5085–5102, https://doi.org/10.5194/bg-13-5085-2016, https://doi.org/10.5194/bg-13-5085-2016, 2016
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Australian vegetation phenology is highly variable due to the diversity of ecosystems on the continent. We explore continental-scale variability using satellite remote sensing by broadly classifying areas as seasonal, non-seasonal, or irregularly seasonal. We also examine ecosystem-scale phenology using phenocams and show that some broadly non-seasonal ecosystems do display phenological variability. Overall, phenocams are useful for understanding ecosystem-scale Australian vegetation phenology.
Rhys Whitley, Jason Beringer, Lindsay B. Hutley, Gab Abramowitz, Martin G. De Kauwe, Remko Duursma, Bradley Evans, Vanessa Haverd, Longhui Li, Youngryel Ryu, Benjamin Smith, Ying-Ping Wang, Mathew Williams, and Qiang Yu
Biogeosciences, 13, 3245–3265, https://doi.org/10.5194/bg-13-3245-2016, https://doi.org/10.5194/bg-13-3245-2016, 2016
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In this study we assess how well terrestrial biosphere models perform at predicting water and carbon cycling for savanna ecosystems. We apply our models to five savanna sites in Northern Australia and highlight key causes for model failure. Our assessment of model performance uses a novel benchmarking system that scores a model’s predictive ability based on how well it is utilizing its driving information. On average, we found the models as a group display only moderate levels of performance.
Caitlin E. Moore, Jason Beringer, Bradley Evans, Lindsay B. Hutley, Ian McHugh, and Nigel J. Tapper
Biogeosciences, 13, 2387–2403, https://doi.org/10.5194/bg-13-2387-2016, https://doi.org/10.5194/bg-13-2387-2016, 2016
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Savannas cover 20 % of the global land surface and account for 25 % of global terrestrial carbon uptake. They support 20 % of the world’s human population and are one of the most important ecosystems on our planet. We evaluated the temporal partitioning of carbon between overstory and understory in Australian tropical savanna using eddy covariance. We found the understory contributed ~ 32 % to annual productivity, increasing to 40 % in the wet season, thus driving seasonality in carbon uptake.
V. Haverd, B. Smith, M. Raupach, P. Briggs, L. Nieradzik, J. Beringer, L. Hutley, C. M. Trudinger, and J. Cleverly
Biogeosciences, 13, 761–779, https://doi.org/10.5194/bg-13-761-2016, https://doi.org/10.5194/bg-13-761-2016, 2016
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We present a new approach for modelling coupled phenology and carbon allocation in savannas, and test it using data from the OzFlux network. Model behaviour emerges from complex feedbacks between the plant physiology and vegetation dynamics, in response to resource availability, and not from imposed hypotheses about the controls on tree-grass co-existence. Results indicate that resource limitation is a stronger determinant of tree cover than disturbance in Australian savannas.
D. Eamus, S. Zolfaghar, R. Villalobos-Vega, J. Cleverly, and A. Huete
Hydrol. Earth Syst. Sci., 19, 4229–4256, https://doi.org/10.5194/hess-19-4229-2015, https://doi.org/10.5194/hess-19-4229-2015, 2015
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In this review, we discuss a range of techniques, including remote sensing, for identifying groundwater-dependent ecosystems and determining rates of water use by GDEs. In addition, gravity recovery satellite data are discussed in relation to changes in soil and groundwater stores. Ecophysiological and structural attributes of GDEs are reviewed, from which we present an integrated ecosystem-scale response as a function of differences in depth-to-groundwater.
V. Haverd, M. R. Raupach, P. R. Briggs, J. G. Canadell, P. Isaac, C. Pickett-Heaps, S. H. Roxburgh, E. van Gorsel, R. A. Viscarra Rossel, and Z. Wang
Biogeosciences, 10, 2011–2040, https://doi.org/10.5194/bg-10-2011-2013, https://doi.org/10.5194/bg-10-2011-2013, 2013
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Daedalus Ionospheric Profile Continuation (DIPCont): Monte Carlo studies assessing the quality of in situ measurement extrapolation
Total Global Solar Radiation Estimation with Relative Humidity and Air Temperature Extremes in Ireland and Holland
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Geosci. Instrum. Method. Data Syst., 12, 239–257, https://doi.org/10.5194/gi-12-239-2023, https://doi.org/10.5194/gi-12-239-2023, 2023
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Motivated by recent community interest in a satellite mission to the atmospheric lower thermosphere and ionosphere (LTI) region (100–200 km altitude), the DIPCont project is concerned with the reconstruction quality of vertical profiles of key LTI variables using dual- and single-spacecraft observations. The report introduces the probabilistic DIPCont modeling framework, demonstrates its usage by means of a set of self-consistent parametric non-isothermal models, and discusses first results.
Can Ekici and Ismail Teke
Geosci. Instrum. Method. Data Syst. Discuss., https://doi.org/10.5194/gi-2017-52, https://doi.org/10.5194/gi-2017-52, 2017
Preprint withdrawn
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Cited articles
Allison, P. D.: Multiple Imputation for Missing Data: A Cautionary Tale,
Sociol. Meth. Res., 28, 301–309, https://doi.org/10.1177/0049124100028003003, 2000.
Altman, D. G. and Bland, J. M.: Missing data, Br. Med. J., 334, 424, https://doi.org/10.1136/bmj.38977.682025.2C, 2007.
Aubinet, M., Grelle, A., Ibrom, A., Rannik, Ü., Moncrieff, J., Foken, T., Kowalski, A. S., Martin, P. H., Berbigier, P., Bernhofer, C., Clement, R., Elbers, J., Granier, A., Grünwald, T., Morgenstern, K., Pilegaard, K., Rebmann, C., Snijders, W., Valentini, R., and Vesala, T.: Estimates of the Annual Net Carbon and Water Exchange of Forests: The EUROFLUX Methodology, Adv. Ecol. Res., 30, 113–175, https://doi.org/10.1016/S0065-2504(08)60018-5, 1999.
Aubinet, M., Vesala, T., and Papale, D.: Eddy Covariance: A Practical Guide
to Measurement and Data Analysis, Springer, Dordrecht, the Netherlands, 2012.
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S.,
Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W., Paw, U. K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: FLUXNET: A New Tool to Study the
Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water
Vapor, and Energy Flux Densities, B. Am. Meteorol. Soc., 82, 2415–2434, https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2, 2001.
Baltagi, B.: Econometric analysis of panel data, available at:
http://www.sidalc.net/cgi-bin/wxis.exe/?IsisScript=book2.xis&method=post&formato=2&cantidad=1&expresion=mfn=001143
(last access: 13 March 2018), 1995.
Barr, A. G., Black, T. A., Hogg, E. H., Kljun, N., Morgenstern, K., and Nesic, Z.: Inter-annual variability in the leaf area index of a boreal
aspen-hazelnut forest in relation to net ecosystem production, Agr. Forest
Meteorol., 126, 237–255, https://doi.org/10.1016/J.AGRFORMET.2004.06.011, 2004.
Barr, A. G., Richardson, A. D., Hollinger, D. Y., Papale, D., Arain, M. A.,
Black, T. A., Bohrer, G., Dragoni, D., Fischer, M. L., Gu, L., Law, B. E.,
Margolis, H. A., Mccaughey, J. H., Munger, J. W., Oechel, W., and Schaeffer,
K.: Use of change-point detection for friction-velocity threshold evaluation
in eddy-covariance studies, Agr. Forest. Meteorol., 171–172, 31–45,
https://doi.org/10.1016/j.agrformet.2012.11.023, 2013.
Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton,
S. H., Jakeman, A. J., Marsili-Libelli, S., Newham, L. T. H., Norton, J. P.,
Perrin, C., Pierce, S. A., Robson, B., Seppelt, R., Voinov, A. A., Fath, B.
D., and Andreassian, V.: Characterising performance of environmental models,
Environ. Model. Softw., 40, 1–20, https://doi.org/10.1016/j.envsoft.2012.09.011, 2013.
Beringer, J., Hutley, L. B., McHugh, I., Arndt, S. K., Campbell, D., Cleugh,
H. A., Cleverly, J., De Dios, V. R., Eamus, D., Evans, B., Ewenz, C., Grace,
P., Griebel, A., Haverd, V., Hinko-Najera, N., Huete, A., Isaac, P., Kanniah, K., Leuning, R., Liddell, M. J., MacFarlane, C., Meyer, W., Moore, C., Pendall, E., Phillips, A., Phillips, R. L., Prober, S. M., Restrepo-Coupe, N., Rutledge, S., Schroder, I., Silberstein, R., Southall, P., Sun Yee, M., Tapper, N. J., Van Gorsel, E., Vote, C., Walker, J., and Wardlaw, T.: An introduction to the Australian and New Zealand flux tower network – OzFlux, Biogeosciences, 13, 5895–5916, https://doi.org/10.5194/bg-13-5895-2016, 2016.
Beringer, J., McHugh, I., Hutley, L. B., Isaac, P., and Kljun, N.: Technical note: Dynamic INtegrated Gap-filling and partitioning for OzFlux (DINGO), Biogeosciences, 14, 1457–1460, https://doi.org/10.5194/bg-14-1457-2017, 2017.
Burba, G. and Anderson, D.: A brief practical guide to eddy covariance flux
measurements: principles and workflow examples for scientific and industrial
applications, available at:
https://books.google.com/books?hl=en&lr=&id=mCsI1_8GdrIC&oi=fnd&pg=PA6&dq=A+Brief+Practical+Guide+to+Eddy+Covariance+Flux+Measurements&ots=TKTg25Yq5X&sig=eBYc819N7Jh3gNhJInfEL1e40eM
(last access: 11 February 2020), 2010.
Chen, T. and Guestrin, C.: XGBoost: A scalable tree boosting system, in: Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 13–17 August 2016, San Francisco, CA, USA, 785–794, https://doi.org/10.1145/2939672.2939785, 2016.
Cleverly, J.: OzFlux data from the Alice Springs Mulga site (AU-ASM), available at: http://data.ozflux.org.au/portal, last access: 9 February 2018.
Cleverly, J., Boulain, N., Villalobos-Vega, R., Grant, N., Faux, R., Wood, C., Cook, P. G., Yu, Q., Leigh, A., and Eamus, D.: Dynamics of component
carbon fluxes in a semi-arid Acacia woodland, central Australia, J. Geophys. Res.-Biogeo., 118, 1168–1185, https://doi.org/10.1002/jgrg.20101, 2013.
Devore, J. L.: Probability and Statistics for Engineering and the Sciences.,
Biometrics, 47, 1638, https://doi.org/10.2307/2532427, 1991.
Dragoni, D., Schmid, H. P., Grimmond, C. S. B., and Loescher, H. W.: Uncertainty of annual net ecosystem productivity estimated using eddy covariance flux measurements, J. Geophys. Res., 112, D17102,
https://doi.org/10.1029/2006JD008149, 2007.
Dreyfus, S. E.: Artificial neural networks, back propagation, and the
kelley-bryson gradient procedure, J. Guid. Control. Dyn., 13, 926–928,
https://doi.org/10.2514/3.25422, 1990.
Drucker, H., Surges, C. J. C., Kaufman, L., Smola, A., and Vapnik, V.:
Support vector regression machines, Adv. Neural Inform. Process. Syst., 1, 155–161, 1997.
Falge, E., Baldocchi, D., Olson, R., Anthoni, P., Aubinet, M., Bernhofer, C., Burba, G., Ceulemans, R., Clement, R., Dolman, H., Granier, A., Gross, P., Grünwald, T., Hollinger, D., Jensen, N. O., Katul, G., Keronen, P., Kowalski, A., Lai, C. T., Law, B. E., Meyers, T., Moncrieff, J., Moors, E.,
Munger, J. W., Pilegaard, K., Rannik, Ü., Rebmann, C., Suyker, A., Tenhunen, J., Tu, K., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: Gap
filling strategies for defensible annual sums of net ecosystem exchange,
Agr. Forest Meteorol., 107, 43–69, https://doi.org/10.1016/S0168-1923(00)00225-2,
2001.
Farley, B. G. and Clark, W. A.: Simulation of self-organizing systems by
digital computer, IRE Prof. Gr. Inf. Theory, 4, 76–84,
https://doi.org/10.1109/TIT.1954.1057468, 1954.
Freedman, D. A.: Statistical Models: Theory and Practice, 2nd Edn., Cambridge
University Press, available at:
https://www.cambridge.org/au/academic/subjects/statistics-probability/statistical-theory-and-methods/statistical-models-theory-and-practice-2nd-edition?format=PB
(last access: 21 March 2020), 2009.
Friedman, J. H.: Greedy Function Approximation: A Gradient Boosting Machine on JSTOR, Ann. Stat., 29, 1189–1232, 2001.
Friedman, J. H.: Stochastic gradient boosting, Comput. Stat. Data Anal., 38, 367–378, https://doi.org/10.1016/S0167-9473(01)00065-2, 2002.
Gani, A., Mohammadi, K., Shamshirband, S., Altameem, T. A., Petković, D.,
and Ch, S.: A combined method to estimate wind speed distribution based on
integrating the support vector machine with firefly algorithm, Environ. Prog. Sustain. Energ., 35, 867–875, https://doi.org/10.1002/ep.12262, 2016.
Géron, A.: Hands-on machine learning with Scikit-Learn and TensorFlow:
concepts, tools, and techniques to build intelligent systems, available at:
https://books.google.com.au/books?hl=en&lr=&id=HHetDwAAQBAJ&oi=fnd&pg=PP1&dq=hands-on+machine+learning+with+&ots=0KvfZqlgOo&sig=5tH2IHRsUaTMTy6CfQ6lw3UDKa4
(last access: 7 February 2020), 2019.
Hagen, S. C., Braswell, B. H., Linder, E., Frolking, S., Richardson, A. D.,
and Hollinger, D. Y.: Statistical uncertainty of eddy flux – Based estimates
of gross ecosystem carbon exchange at Howland Forest, Maine, J. Geophys.
Res.-Atmos., 111, 1–12, https://doi.org/10.1029/2005JD006154, 2006.
Harrell, F. E.: Regression Modeling Strategies: With Applications to Linear
Models, Logistic, available at:
https://books.google.com.au/books?hl=en&lr=&id=94RgCgAAQBAJ&oi=fnd&pg=PR7&dq=regression+modeling+strategies+frank+harrell&ots=ZAt4RsaS1r&sig=mikE1s9G4IXzqZKEie-iVA9GTV0&redir_esc=y#v=onepage&q=regression modeling strategies frankharrell&f=false (last access: 11 February 2020), 2014.
Harvey, A. C. and Peters, S.: Estimation procedures for structural time series models, J. Forecast., 9, 89–108, https://doi.org/10.1002/for.3980090203, 1990.
Haverd, V., Briggs, P., Trudinger, C., Nieradzik, L., and Canadell, P.: BIOS2
– Frontier Modelling of the Australian Carbon and Water Cycles, CSIRO, Hobart, Tasmania, Australia, 2015.
Ho, T. K.: Random decision forests, in: Proc. Int. Conf. Doc. Anal. Recognition, ICDAR, 14–16 August 1995, Montreal, QC, Canada, 278–282, https://doi.org/10.1109/ICDAR.1995.598994, 1995.
Ho, T. K.: The Random Subspace Method for Constructing Decision Forests, IEEE T. Pattern Anal. Mac. Intel., 20, 832–844, 1998.
Hollinger, D. Y., Goltz, S. M., Davidson, E. A., Lee, J. T., Tu, K., and
Valentine, H. T.: Seasonal patterns and environmental control of carbon dioxide and water vapour exchange in an ecotonal boreal forest, Global Change
Biol., 5, 891–902, https://doi.org/10.1046/j.1365-2486.1999.00281.x, 1999.
Hsiao, C., Hashem Pesaran, M., and Kamil Tahmiscioglu, A.: Maximum likelihood
estimation of fixed effects dynamic panel data models covering short time
periods, J. Econom., 109, 107–150, https://doi.org/10.1016/S0304-4076(01)00143-9, 2002.
Hui, D., Wan, S., Su, B., Katul, G., Monson, R., and Luo, Y.: Gap-filling
missing data in eddy covariance measurements using multiple imputation (MI)
for annual estimations, Agr. Forest Meteorol., 121, 93–111,
https://doi.org/10.1016/S0168-1923(03)00158-8, 2004.
Hutley, L. B., Leuning, R., Beringer, J., and Cleugh, H. A.: The utility of
the eddy covariance technique as a tool in carbon accounting: tropical savanna as a case study, Aust. J. Bot., 53, 663–675, 2005.
Isaac, P., Cleverly, J., McHugh, I., Van Gorsel, E., Ewenz, C., and Beringer,
J.: OzFlux data: Network integration from collection to curation, Biogeosciences, 14, 2903–2928, https://doi.org/10.5194/bg-14-2903-2017, 2017.
Izady, A., Davary, K., Alizadeh, A., Moghaddam Nia, A., Ziaei, A. N., and
Hasheminia, S. M.: Application of NN-ARX Model to Predict Groundwater Levels
in the Neishaboor Plain, Iran, Water Resour. Manage., 27, 4773–4794,
https://doi.org/10.1007/s11269-013-0432-y, 2013.
Izady, A., Abdalla, O., and Mahabbati, A.: Dynamic panel-data-based groundwater level prediction and decomposition in an arid hardrock–alluvium
aquifer, Environ. Earth Sci., 75, 1–13, https://doi.org/10.1007/s12665-016-6059-6, 2016.
Kang, H.: The prevention and handling of the missing data, Korean J.
Anesthesiol., 64, 402–406, https://doi.org/10.4097/kjae.2013.64.5.402, 2013.
Kim, Y., Johnson, M. S., Knox, S. H., Black, T. A., Dalmagro, H. J., Kang, M., Kim, J., and Baldocchi, D.: Gap-filling approaches for eddy covariance
methane fluxes: A comparison of three machine learning algorithms and a
traditional method with principal component analysis, Global Change Biol., 26, 1499–1518, https://doi.org/10.1111/gcb.14845, 2020.
Kock, N. and Gaskins, L.: Simpson's paradox, moderation and the emergence of
quadratic relationships in path models: an information systems illustration,
Int. J. Appl. Nonlin. Sci., 2, 200–234, https://doi.org/10.1504/ijans.2016.077025, 2016.
Kunwor, S., Starr, G., Loescher, H. W., and Staudhammer, C. L.: Preserving
the variance in imputed eddy-covariance measurements: Alternative methods for defensible gap filling, Agr. Forest Meteorol., 232, 635–649,
https://doi.org/10.1016/j.agrformet.2016.10.018, 2017.
Law, B. E., Falge, E., Gu, L., Baldocchi, D. D., Bakwin, P., Berbigier, P.,
Davis, K., Dolman, A. J., Falk, M., Fuentes, J. D., Goldstein, A., Granier,
A., Grelle, A., Hollinger, D., Janssens, I. A., Jarvis, P., Jensen, N. O.,
Katul, G., Mahli, Y., Matteucci, G., Meyers, T., Monson, R., Munger, W., Oechel, W., Olson, R., Pilegaard, K., Paw U H, K. T., Thorgeirsson, H.,
Valentini, R., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: Jourassess2,
Agr. Forest Meteorol., 113, 97–120, 2002.
Lee, X., Fuentes, J. D., Staebler, R. M., and Neumann, H. H.: Long-term
observation of the atmospheric exchange of CO2 with a temperate deciduous forest in southern Ontario, Canada, J. Geophys. Res.-Atmos., 104,
15975–15984, https://doi.org/10.1029/1999JD900227, 1999.
Little, R. J. A.: Statistical analysis with missing data, 2nd Edn., edited by: Rubin, D. B., Wiley, Hoboken, NJ, 2002.
Mahabbati, A. (Creator): A comparison of gap-filling algorithms for eddy covariance fluxes and their drivers, The University of Western Australia, AliceSpringsMulga_AWS(.nc), AliceSpringsMulga_BIOS2(.nc), AliceSpringsMulga_ACCESS(.nc), AliceSpringsMulga_L3(.nc), AliceSpringsMulga_L4(.nc), Calperum_AWS(.nc), Calperum_BIOS2(.nc), Calperum_L3(.nc), Calperum_L4(.nc), Calperum_ACCESS(.nc), Gingin_AWS(.nc), Gingin_ACCESS(.nc), Gingin_BIOS2(.nc), Gingin_L3(.nc), Gingin_L4(.nc), HowardSprings_AWS(.nc), HowardSprings_BIOS2(.nc), HowardSprings_ACCESS(.nc), HowardSprings_L4(.nc), Tumbarumba_ACCESS(.nc), HowardSprings_L3(.nc), Tumbarumba_BIOS2(.nc), Tumbarumba_L3(.nc), Tumbarumba_L4(.nc), Tumbarumba_AWS(.nc), https://doi.org/10.26182/5f292ee80a0c0, 2020.
Mahabbati, A., Izady, A., Mousavi Baygi, M., Davary, K., and Hasheminia, S. M.: Daily soil temperature modeling using `panel-data' concept, J. Appl.
Stat., 44, 1385–1401, https://doi.org/10.1080/02664763.2016.1214240, 2017.
Menzer, O., Moffat, A. M., Meiring, W., Lasslop, G., Schukat-Talamazzini, E.
G., and Reichstein, M.: Random errors in carbon and water vapor fluxes assessed with Gaussian Processes, Agr. Forest Meteorol., 178–179, 161–172,
https://doi.org/10.1016/j.agrformet.2013.04.024, 2013.
Moffat, A. M., Papale, D., Reichstein, M., Hollinger, D. Y., Richardson, A.
D., Barr, A. G., Beckstein, C., Braswell, B. H., Churkina, G., Desai, A. R.,
Falge, E., Gove, J. H., Heimann, M., Hui, D., Jarvis, A. J., Kattge, J., Noormets, A., and Stauch, V. J.: Comprehensive comparison of gap-filling
techniques for eddy covariance net carbon fluxes, Agr. Forest Meteorol., 147, 209–232, https://doi.org/10.1016/j.agrformet.2007.08.011, 2007.
Molenberghs, G., Fitzmaurice, G., Kenward, M. G., Tsiatis, A., Verbeke, G.,
Fitzmaurice, G., Kenward, M. G., Tsiatis, A., and Verbeke, G.: Handbook of
Missing Data Methodology, Chapman and Hall/CRC, Boca Raton, Florida, 2014.
Ogle, K., Barber, J. J., Barron-Gafford, G. A., Bentley, L. P., Young, J. M., Huxman, T. E., Loik, M. E., and Tissue, D. T.: Quantifying ecological memory in plant and ecosystem processes, Ecol. Lett., 18, 221–235, https://doi.org/10.1111/ele.12399, 2015.
Papale, D. and Valentini, R.: A new assessment of European forests carbon
exchanges by eddy fluxes and artificial neural network spatialization, Global
Change Biol., 9, 525–535, https://doi.org/10.1046/j.1365-2486.2003.00609.x, 2003.
Pilegaard, K., Hummelshøj, P., Jensen, N. O., and Chen, Z.: Two years of
continuous CO2 eddy-flux measurements over a Danish beech forest, Agr. Forest Meteorol., 107, 29–41, https://doi.org/10.1016/S0168-1923(00)00227-6, 2001.
Reichle, R. H., Koster, R. D., Dong, J., and Berg, A. A.: Global soil moisture from satellite observations, land surface models, and ground data:
Implications for data assimilation, J. Hydrometeorol., 5, 430–442,
https://doi.org/10.1175/1525-7541(2004)005<0430:GSMFSO>2.0.CO;2, 2004.
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grünwald, T., Havránková, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J. M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and Valentini, R.: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: Review and improved algorithm, Global Change Biol., 11, 1424–1439, https://doi.org/10.1111/j.1365-2486.2005.001002.x, 2005.
Richardson, A. D. and Hollinger, D. Y.: A method to estimate the additional
uncertainty in gap-filled NEE resulting from long gaps in the CO2 flux record, Agr. Forest Meteorol., 147, 199–208, https://doi.org/10.1016/j.agrformet.2007.06.004, 2007.
Richardson, A. D., Braswell, B. H., Hollinger, D. Y., Burman, P., Davidson, E. A., Evans, R. S., Flanagan, L. B., Munger, J. W., Savage, K., Urbanski, S. P., and Wofsy, S. C.: Comparing simple respiration models for eddy flux and dynamic chamber data, Agr. Forest Meteorol., 141, 219–234,
https://doi.org/10.1016/J.AGRFORMET.2006.10.010, 2006.
Richardson, A. D., Aubinet, M., Barr, A. G., Hollinger, D. Y., Ibrom, A.,
Lasslop, G., and Reichstein, M.: Uncertainty Quantification, in: Eddy
Covariance, Springer, Dordrecht, the Netherlands, 173–209, 2012.
Sahoo, A. K., Dirmeyer, P. A., Houser, P. R., and Kafatos, M.: A study of
land surface processes using land surface models over the Little River
Experimental Watershed, Georgia, J. Geophys. Res.-Atmos., 113, D20121,
https://doi.org/10.1029/2007JD009671, 2008.
Scanlon, T. M. and Kustas, W. P.: Partitioning carbon dioxide and water vapor fluxes using correlation analysis, Agr. Forest Meteorol., 150, 89–99, https://doi.org/10.1016/j.agrformet.2009.09.005, 2010.
Scanlon, T. M. and Sahu, P.: On the correlation structure of water vapor and
carbon dioxide in the atmospheric surface layer: A basis for flux partitioning, Water Resour. Res., 44, W10418, https://doi.org/10.1029/2008WR006932, 2008.
Staebler, M.: Long-term observation of the atmospheric exchange of CO2 with a temperate deciduous forest in southern Ontario, Canada ecosystem net ecosystem production turbulence is turbulent, Data Process, 104, 975–984, 1999.
Tannenbaum, C. E.: The empirical nature and statistical treatment of missing
data., Diss. Abstr. Int. Sect. A Humanit. Soc. Sci., available at:
http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=psyc7&NEWS=N&AN=$2010-99071-044
(last access: 20 February 2018), 2010.
Taylor, S. J. and Letham, B.: Forecasting at Scale, Am. Stat., 72, 37–45, https://doi.org/10.1080/00031305.2017.1380080, 2018.
Tenhunen, J. D., Valentini, R., Köstner, B., Zimmermann, R., and Granier,
A.: Variation in forest gas exchange at landscape to continental scales, Ann. Sci. For., 55, 1–11, https://doi.org/10.1051/forest:19980101, 1998.
Wooldridge, J. M.: Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge, 2002.
Ye, J., Chow, J.-H., Chen, J., and Zheng, Z.: Stochastic gradient boosted
distributed decision trees, in: Proceeding of the 18th ACM conference on
Information and knowledge management – CIKM'09, ACM Press, New York, USA, p. 2061, 2009.
Zhao, X. and Huang, Y.: A comparison of three gap filling techniques for eddy covariance net carbon fluxes in short vegetation ecosystems, Adv. Meteorol., 2015, 1–12, https://doi.org/10.1155/2015/260580, 2015.
Zou, H. and Hastie, T.: Regularization and variable selection via the
elastic net, available at:
https://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=22250F01CC77D55C54B6BAFF4512C9E3?doi=10.1.1.124.4696&rep=rep1&type=pdf
(last access: 28 August 2019), 2005.
Short summary
We reviewed eight algorithms to estimate missing values of environmental drivers and three major fluxes in eddy covariance time series. Overall, machine-learning algorithms showed superiority over the rest. Among the top three models (feed-forward neural networks, eXtreme Gradient Boost, and random forest algorithms), the latter showed the most solid performance in different scenarios.
We reviewed eight algorithms to estimate missing values of environmental drivers and three major...
