English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes

MPS-Authors
/persons/resource/persons62489

Moffat,  A. M.
Department Biogeochemical Systems, Prof. M. Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society;

/persons/resource/persons62524

Reichstein,  M.
Research Group Biogeochemical Model-data Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

/persons/resource/persons62356

Churkina,  G.
Department Biogeochemical Systems, Prof. M. Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society;

/persons/resource/persons62402

Heimann,  M.
Department Biogeochemical Systems, Prof. M. Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society;

/persons/resource/persons62433

Kattge,  Jens
TRY: Global Initiative on Plant Traits, Dr. J. Kattge, Research Group Organismic Biogeochemistry, Dr. C. Wirth, Max Planck Institute for Biogeochemistry, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Moffat, A. M., Papale, D., Reichstein, M., Hollinger, D. Y., Richardson, A. D., Barr, A. G., et al. (2007). Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes. Agricultural and Forest Meteorology, 147(3-4), 209-232. doi:10.1016/j.agrformet.2007.08.011.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000E-D574-E
Abstract
We review 15 techniques for estimating missing values of net ecosystem CO2 exchange (NEE) in eddy covariance time series and evaluate their performance for different artificial gap scenarios based on a set of 10 benchmark datasets from six forested sites in Europe. The goal of gap filling is the reproduction of the NEE time series and hence this present work focuses on estimating missing NEE values, not on editing or the removal of suspect values in these time series due to systematic errors in the measurements (e.g., nighttime flux, advection). The gap filling was examined by generating 50 secondary datasets with artificial gaps (ranging in length from single half-hours to 12 consecutive days) for each benchmark dataset and evaluating the performance with a variety of statistical metrics. The performance of the gap filling varied among sites and depended on the level of aggregation (native half-hourly time step versus daily), long gaps were more difficult to fill than short gaps, and differences among the techniques were more pronounced during the day than at night. The non-linear regression techniques (NLRs), the look-up table (LUT), marginal distribution sampling (MDS), and the semiparametric model (SPM) generally showed good overall performance. The artificial neural network based techniques (ANNs) were generally, if only slightly, superior to the other techniques. The simple interpolation technique of mean diurnal variation (MDV) showed a moderate but consistent performance. Several sophisticated techniques, the dual unscented Kalman filter (UKF), the multiple imputation method (MIM), the terrestrial biosphere model (BETHY), but also one of the ANNs and one of the NLRs showed high biases which resulted in a low reliability of the annual sums, indicating that additional development might be needed. An uncertainty analysis comparing the estimated random error in the 10 benchmark datasets with the artificial gap residuals suggested that the techniques are already at or very close to the noise limit of the measurements. Based on the techniques and site data examined here, the effect of gap filling on the annual sums of NEE is modest, with most techniques falling within a range of +/- 25 g C m(-2) year(-1.) (c) 2007 Elsevier B.V All rights reserved. [References: 49]