Evaluation of statistical gap fillings for continuous energy flux (evapotranspiration) measurements for two different land cover types

  • Jongmin Park
  • Kyuhyun Byun
  • Minha Choi
  • Ehsun Jang
  • Junghoon Lee
  • Yeonkil Lee
  • Sungwon Jung
Original Paper


Over the past few decades, energy and water fluxes have been directly measured by a global flux network, which was established by regional and continental network sites based on an eddy covariance (EC) method. Although, the EC method possesses many advantages, its typical data coverage could not exceed 65 % due to various environmental factors including micrometeorological conditions and systematic malfunctions. In this study, four different methodologies were used to fill the gap in latent heat flux (LE) data. These methods were Food and Agriculture Organization Penman–Monteith (FAO_PM) equation, mean diurnal variation (MDV), Kalman filter, and dynamic linear regression (DLR). We used these methods to evaluate two flux towers at different land cover types located at Seolmacheon (SMC) and Cheongmicheon (CMC) in Korea. The LE estimated by four different approaches was a fairly close match to the observed LE, with the root mean square error ranging from 4.81 to 61.88 W m−2 at SMC and from 0.89 to 60.27 W m−2 at CMC. At both sites, the LE estimated by DLR showed the best result with the value of the coefficient of correlation (R), equal to 0.99. Cost-effectiveness analysis for evaluating four different gap-filling methods also confirmed that DLR showed the best cost effectiveness ratio (C/R). The Kalman filter showed the second highest C/R rank except in the winter season at SMC followed by MDV and FAO_PM. Energy closures with estimated LE led to further improved compare to the energy closure of the observed LE. The results showed that the estimated LE at CMC was a better fit with the observed LE than the estimated LE at SMC due to the more complicated topography and land cover at the SMC site. This caused more complex interactions between the surface and the atmosphere. The estimated LE with all approaches used in this study showed improvement in energy closure at both sites. The results of this study suggest that each method can be used as a gap-filling model for LE. However, it is important to consider the strengths and weaknesses of each method, the purpose of research, characteristics of the study site, study period and data availability.


Flux tower Eddy covariance Latent heat flux Gap-filling 



This research was supported by the Space core technology development program through the National Research Foundation of Korea (NRF), which is funded by the Ministry of Science, ICT and future planning (NRF-2014M1A3A3A02034789). We would like to thank the Hydrological Survey Center (HSC) for providing flux tower data at SMC and CMC.


  1. Alavi N, Warland JS, Berg AA (2006) Filling gaps in evapotranspiration measurements for water budget studies: evaluation of a Kalman filtering approach. Agric For Meteorol 141:57–66CrossRefGoogle Scholar
  2. Allen RG, Pereira LS (2009) Estimating crop coefficients from fraction of ground cover and height. Irrig Sci 28:17–34CrossRefGoogle Scholar
  3. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56 FAO, RomeGoogle Scholar
  4. Amthor JS (1995) Terrestrial higher-plant response to increasing atmospheric (CO2) in relation to the global carbon cycle. Glob Chang Biol 1:243–274CrossRefGoogle Scholar
  5. Aubinet M, Vesala T, Papale D (2012) Eddy covariance: a practical guide to measurement and data analysis. Springer, New YorkCrossRefGoogle Scholar
  6. Baldocchi D et al (2001) FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull Am Meteorol Soc 82:2415–2434CrossRefGoogle Scholar
  7. Bamberger I, Hörtnagl L, Walser M, Hansel A, Wohlfahrt G (2014) Gap-filling strategies for annual VOC flux data sets. Biogeosciences 11:2429–2442CrossRefGoogle Scholar
  8. Bi X et al (2007) Seasonal and diurnal variations in moisture, heat, and CO2 fluxes over grassland in the tropical monsoon region of southern China. J Geophys Res: Atmos 112:D10106. doi: 10.1029/2006jd007889 CrossRefGoogle Scholar
  9. Byun K, Liaqat UW, Choi M (2014) Dual-model approaches for evapotranspiration analyses over homo-and heterogeneous land surface conditions. Agric For Meteorol 197:169–187CrossRefGoogle Scholar
  10. Cava D, Contini D, Donateo A, Martano P (2008) Analysis of short-term closure of the surface energy balance above short vegetation. Agric For Meteorol 148:82–93CrossRefGoogle Scholar
  11. Chatfield C (2013) The analysis of time series: an introduction. CRC Press, Boca RatonGoogle Scholar
  12. Chen YY, Chu CR, Li MH (2012) A gap-filling model for eddy covariance latent heat flux: estimating evapotranspiration of a subtropical seasonal evergreen broad-leaved forest as an example. J Hydrol 468–469:101–110Google Scholar
  13. Choi M (2013) Parameterizing daytime downward longwave radiation in two Korean regional flux monitoring network sites. J Hydrol 476:257–264CrossRefGoogle Scholar
  14. Choi M, Lee SO, Kwon H (2010) Understanding of the common land model performance for water and energy fluxes in a farmland during the growing season in Korea. Hydrol Process 24:1063–1071CrossRefGoogle Scholar
  15. Costa M, Gonçalves AM (2011) Clustering and forecasting of dissolved oxygen concentration on a river basin. Stoch Environ Res Risk Assess 25:151–163CrossRefGoogle Scholar
  16. Detto M, Verfaillie J, Anderson F, Xu L, Baldocchi D (2011) Comparing laser-based open- and closed-path gas analyzers to measure methane fluxes using the eddy covariance method. Agric For Meteorol 151:1312–1324CrossRefGoogle Scholar
  17. Exner-Kittridge MG, Rains MC (2010) Case study on the accuracy and cost/effectiveness in simulating reference evapotranspiration in West-Central Florida. J Hydrol Eng 15:696–703CrossRefGoogle Scholar
  18. Falge E et al (2001) Gap filling strategies for defensible annual sums of net ecosystem exchange. Agric For Meteorol 107:43–69CrossRefGoogle Scholar
  19. Foken T, Wimmer F, Mauder M, Thomas C, Liebethal C (2006) Some aspects of the energy balance closure problem. Atmos Chem Phys 6:4395–4402CrossRefGoogle Scholar
  20. Garcia M, Raes D, Allen R, Herbas C (2004) Dynamics of reference evapotranspiration in the Bolivian highlands (Altiplano). Agric For Meteorol 125:67–82CrossRefGoogle Scholar
  21. Göckede M, Rebmann C, Foken T (2004) A combination of quality assessment tools for eddy covariance measurements with footprint modelling for the characterisation of complex sites. Agric For Meteorol 127:175–188CrossRefGoogle Scholar
  22. Gonçalves AM, Costa M (2013) Predicting seasonal and hydro-meteorological impact in environmental variables modelling via Kalman filtering. Stoch Environ Res Risk Assess 27:1021–1038CrossRefGoogle Scholar
  23. Hansen JE, Sato M, Lacis A, Ruedy R, Tegen I, Matthews E (1998) Climate forcings in the Industrial era. Proc Natl Acad Sci USA 95:12753–12758CrossRefGoogle Scholar
  24. Hollinger DY, Richardson AD (2005) Uncertainty in eddy covariance measurements and its application to physiological models. Tree Physiol 25:873–885CrossRefGoogle Scholar
  25. Hong J, Kim J, Lee D, Lim JH (2008) Estimation of the storage and advection effects on H2O and CO2 exchanges in a hilly KoFlux forest catchment. Water Resour Res 44Google Scholar
  26. Hong J, Kwon H, Lim J, Byun Y, Lee J, Kim J (2009) Standardization of KoFlux eddy-covariance data processing. Korean J Agric For Meteorol 11:19–26CrossRefGoogle Scholar
  27. Hong J, Takagi K, Ohta T, Kodama Y (2014) Wet surface resistance of forest canopy in monsoon Asia: implications for eddy-covariance measurement of evapotranspiration. Hydrol Process 28:37–42CrossRefGoogle Scholar
  28. IPCC (2014) Climate change 2013: the physical science basis: working group I contribution to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  29. Jarvis AJ, Stauch VJ, Schulz K, Young PC (2004) The seasonal temperature dependency of photosynthesis and respiration in two deciduous forests. Glob Chang Biol 10:939–950CrossRefGoogle Scholar
  30. Kalman RE (1960) A new approach to linear filtering and prediction problems. J Fluids Eng 82:35–45Google Scholar
  31. Kato T, Tang Y, Gu S, Hirota M, Du M, Li Y, Zhao X (2006) Temperature and biomass influences on interannual changes in CO2 exchange in an alpine meadow on the Qinghai-Tibetan Plateau. Glob Chang Biol 12:1285–1298CrossRefGoogle Scholar
  32. Kwon HJ, Lee JH, Lee YK, Lee JW, Jung SW, Kim J (2009) Seasonal variations of evapotranspiration observed in a mixed forest in the Seolmacheon catchment. Korean J Agric For Meteorol 11:39–47CrossRefGoogle Scholar
  33. Lee X, Massman WJ (2011) A perspective on thirty years of the Webb, Pearman and Leuning density corrections. Bound-Layer Meteorol 139:37–59CrossRefGoogle Scholar
  34. Lee X, Massman W, Law BE (2006) Handbook of micrometeorology: a guide for surface flux measurement and analysis, vol 29. Springer, New YorkGoogle Scholar
  35. Li Z, Yu G, Wen X, Zhang L, Ren C, Fu Y (2005) Energy balance closure at ChinaFLUX sites. Sci China Ser D 48:51–62Google Scholar
  36. Lindroth A, Mölder M, Lagergren F (2010) Heat storage in forest biomass improves energy balance closure. Biogeosciences 7:301–313. doi: 10.5194/bg-7-301-2010 CrossRefGoogle Scholar
  37. Loescher HW, Law BE, Mahrt L, Hollinger DY, Campbell J, Wofsy SC (2006) Uncertainties in, and interpretation of, carbon flux estimates using the eddy covariance technique. J Geophys Res D 111Google Scholar
  38. Mahrt L (1998) Flux sampling errors for aircraft and towers. J Atmos Ocean Technol 15:416–429CrossRefGoogle Scholar
  39. Massman WJ (2000) A simple method for estimating frequency response corrections for eddy covariance systems. Agric For Meteorol 104:185–198CrossRefGoogle Scholar
  40. Mo X, Chen JM, Ju W, Black TA (2008) Optimization of ecosystem model parameters through assimilating eddy covariance flux data with an ensemble Kalman filter. Ecol Model 217:157–173CrossRefGoogle Scholar
  41. Moffat AM et al (2007) Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes. Agric For Meteorol 147:209–232CrossRefGoogle Scholar
  42. Novick K, Brantley S, Miniat CF, Walker J, Vose J (2014) Inferring the contribution of advection to total ecosystem scalar fluxes over a tall forest in complex terrain. Agric For Meteorol 185:1–13CrossRefGoogle Scholar
  43. Ocana-Peinado F, Valderrama M, Aguilera A (2008) A dynamic regression model for air pollen concentration. Stoch Environ Res Risk Assess 22:59–63. doi: 10.1007/s00477-007-0153-y CrossRefGoogle Scholar
  44. Pagowski M et al (2006) Application of dynamic linear regression to improve the skill of ensemble-based deterministic ozone forecasts. Atmos Environ 40:3240–3250CrossRefGoogle Scholar
  45. Papale D (2006) Towards a standardized processing of net ecosystem exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences 3:571–583CrossRefGoogle Scholar
  46. Samain O, Roujean JL, Geiger B (2008) Use of a Kalman filter for the retrieval of surface BRDF coefficients with a time-evolving model based on the ECOCLIMAP land cover classification. Remote Sens Environ 112:1337–1346CrossRefGoogle Scholar
  47. Schmid HP (1994) Source areas for scalars and scalar fluxes. Bound-Layer Meteorol 67:293–318CrossRefGoogle Scholar
  48. Stoy PC et al (2013) A data-driven analysis of energy balance closure across FLUXNET research sites: the role of landscape scale heterogeneity. Agric For Meteorol 171–172:137–152CrossRefGoogle Scholar
  49. Todd RW, Evett SR, Howell TA (2000) The Bowen ratio-energy balance method for estimating latent heat flux of irrigated alfalfa evaluated in a semi-arid, advective environment. Agric For Meteorol 103:335–348CrossRefGoogle Scholar
  50. Twine TE et al (2000) Correcting eddy-covariance flux underestimates over a grassland. Agric For Meteorol 103:279–300CrossRefGoogle Scholar
  51. Vickers D, Mahrt L (1997) Quality control and flux sampling problems for tower and aircraft data. J Atmos Ocean Technol 14:512–526CrossRefGoogle Scholar
  52. Vinukollu RK, Wood EF, Ferguson CR, Fisher JB (2011) Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: evaluation of three process-based approaches. Remote Sens Environ 115:801–823CrossRefGoogle Scholar
  53. Webb EK, Pearman GI, Leuning R (1980) Correction of flux measurements for density effects due to heat and water vapour transfer. Q J R Meteorol Soc 106:85–100CrossRefGoogle Scholar
  54. Whittle P (1953) The analysis of multiple stationary time series. J R Stat Soc Ser B (Methodological) 125–139Google Scholar
  55. Wilczak JM, Oncley SP, Stage SA (2001) Sonic anemometer tilt correction algorithms. Bound-Layer Meteorol 99:127–150CrossRefGoogle Scholar
  56. Wilson K et al (2002) Energy balance closure at FLUXNET sites. Agric For Meteorol 113:223–243CrossRefGoogle Scholar
  57. Young PC (1999) Nonstationary time series analysis and forecasting. Prog Environ Sci 1:3–48Google Scholar
  58. Young PC (2011) Recursive estimation and time-series analysis: an introduction for the student and practitioner. Springer, New YorkCrossRefGoogle Scholar
  59. Young PC, Pedregal DJ (1999) Recursive and en-bloc approaches to signal extraction. J Appl Stat 26:103–128CrossRefGoogle Scholar
  60. Young PC, Taylor CJ, Tych W, Pedregal DJ (2007) The Captain Toolbox. Centre for Research on Environmental Systems and Statistics The Captain Toolbox Centre for Research on Environmental Systems and StatisticsGoogle Scholar
  61. Yuan R, Kang M, Park S, Hong J, Lee D, Kim J (2007) The effect of coordinate rotation on the eddy covariance flux estimation in a hilly KoFlux forest catchment. Korean J Agric For Meteorol 9:8Google Scholar
  62. Yuan R, Kang M, Park S, Hong J, Lee D, Kim J (2011) Expansion of the planar-fit method to estimate flux over complex terrain. Meteorol Atmos Phys 110:123–133CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Jongmin Park
    • 1
  • Kyuhyun Byun
    • 2
  • Minha Choi
    • 1
  • Ehsun Jang
    • 3
  • Junghoon Lee
    • 4
  • Yeonkil Lee
    • 4
  • Sungwon Jung
    • 4
  1. 1.Water Resources and Remote Sensing Laboratory, Department of Water Resources, Graduate School of Water ResourcesSungkyunkwan UniversitySuwonRepublic of Korea
  2. 2.Department of Civil& Environmental Engineering & Earth SciencesUniversity of Notre DameNotre DameUnited States
  3. 3.Department of Civil and Environmental EngineeringHanyang UniversitySeoulRepublic of Korea
  4. 4.Hydrological Survey CenterGoyangRepublic of Korea

Personalised recommendations