Flux footprint climatology estimated by three analytical models over a subtropical coniferous plantation in Southeast China

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• 作者：Hui Zhang ; Xuefa Wen
• 关键词：eddy covariance ; flux footprint ; flux footprint climatology ; model comparison
• 刊名：Acta Meteorologica Sinica
• 出版年：2015
• 出版时间：August 2015
• 年：2015
• 卷：29
• 期：4
• 页码：654-666
• 全文大小：1,004 KB
• 参考文献：Allen, R. G., L. S. Pereira, T. A. Howell, et al., 2011: Evapotranspiration information reporting. I: Factors governing measurement accuracy. Agr. Water Manage., 98, 899-20.CrossRef
  Amiro, B. D., 1998: Footprint climatologies for evapotranspiration in a boreal catchment. Agr. Forest. Meteor., 90, 195-01.CrossRef
  Aubinet, M., T. Vesala, and D. Papale, 2012: Eddy Covariance: A Practical Guide to Measurement and Data Analysis. Springer, New York, 21-8 pp.CrossRef
  Baldocchi, D. D., 2008: “Breathing-of the terrestrial biosphere: Lessons learned from a global network of carbon dioxide flux measurement systems. Aust. J. Bot., 56, 1-6.CrossRef
  Biermann, T., W. Babel, E. Thiem, et al., 2011: Energy fluxes above Nam Co Lake and the surrounding grassland—The NamCo 2009 experiment. 7th Sino-German Workshop on Tibetan Plateau Research, Hamburg, Germany, 3- March, German TiP project and Institute of Tibetan Plateau Research.
  Cai, X. H., J. Y. Chen, and R. L. Desjardins, 2010: Flux footprints in the convective boundary layer: Large-eddy simulation and lagrangian stochastic modelling. Bound.-Layer Meteor., 137, 31-7.CrossRef
  Cai, X. H., M. J. Zhu, S. M. Liu, et al., 2011: Flux footprint analysis and application for the large aperture scintillometer. Adv. Earth. Sci., 25, 1166-174.
  Chen, B., Q. Ge, D. Fu, et al., 2010: A data-model fusion approach for upscaling gross ecosystem productivity to the landscape scale based on remote sensing and flux footprint modelling. Biogeosciences, 7, 2943-958.CrossRef
  Chen, B. Z., J. M. Chen, G. Mo, et al., 2008: Comparison of regional carbon flux estimates from CO2 concentration measurements and remote sensing based footprint integration. Global Biogeochem. Cycle, 22, doi: 10.1029/2007GB003024.
  Chen, B. Z., N. C. Coops, D. J. Fu, et al., 2011: Assessing eddy-covariance flux tower location bias across the fluxnet–Canada research network based on remote sensing and footprint modelling. Agr. Forest Meteor., 151, 87-00.CrossRef
  Chen, B. Z., N. C. Coops, D. J. Fu, et al., 2012: Characterizing spatial representativeness of flux tower eddy-covariance measurements across the Canadian carbon program network using remote sensing and footprint analysis. Remote Sens. Environ., 124, 742-55.CrossRef
  Chen, B. Z., T. A. Black, N. C. Coops, et al., 2009: Assessing tower flux footprint climatology and scaling between remotely sensed and eddy covariance measurements. Bound.-Layer Meteor., 130, 137-67.CrossRef
  Foken, T., 2008: Micrometeorology. Springer-Verlag, Berlin, Heidelberg, 82-7.
  Gash, J. H. C., 1986: A note on estimating the effect of a limited fetch on micrometeorological evaporation measurements. Bound.-Layer Meteor., 35, 409-13.CrossRef
  Gckede, M., T. Markkanen, C. B. Hasager, et al., 2006: Update of a footprint-based approach for the characterisation of complex measurement sites. Bound. -Layer Meteor., 118, 635-55.CrossRef
  Gckede, M., T. Foken, M. Aubinet, et al., 2008: Quality control of CarboEurope flux data. Part 1: Coupling footprint analyses with flux data quality assessment to evaluate sites in forest ecosystems. Biogeosciences, 5, 433-50.CrossRef
  Horst, T. W., and J. C. Weil, 1992: Footprint estimation for scalar flux measurements in the atmospheric surface layer. Bound.-Layer Meteor., 59, 279-96.CrossRef
  Hsieh, C. I., G. Katul, and T. W. Chi, 2000: An approximate analytical model for footprint estimation of scalar fluxes in thermally stratified atmospheric flows. Adv. Water Resour., 23, 765-72.CrossRef
  Kljun, N., 2010: Attributing tall tower flux data to heterogeneous vegetation. 19th Symposium on Boundary Layers and Turbulence, Keystone, Colorado, 2- August, Amer. Meteor. Soc.
  Kljun, N., 2010: BERMS sites revisited: Footprint climatology and 3D-LiDAR data. 29th Conference on Agricultural and Forest Meteorology, Keystone, Colorado, 2- August, Amer. Meteor. Soc.
  Kormann, R., and F. X. Meixner, 2001: An analytical footprint model for non-neutral stratification. Bound.-Layer Meteor., 99, 207-24.CrossRef
  Leclerc, M. Y., and G. W. Thurtell, 1990: Footprint prediction of scalar fluxes using a Markovian analysis. Bound.-Layer Meteor., 52, 247-58.CrossRef
  Leclerc, M. Y., and T. Foken, 2014: Footprints in Micrometeorology and Ecology. Springer-Verlag, Berlin, Heidelberg, 71-8.
  Leuning, R., 2005: Measurements of trace gas fluxes in the atmosphere using eddy covariance: WPL corrections revisited. Handbook of Micrometeorology: A Guide for Surface Flux Measurement and Analysis. Springer, Netherlands, 119-32.CrossRef
  Rebmann, C., M. Gockede, T. Foken, et al., 2005: Quality analysis applied on eddy covariance measurements at complex forest sites using footprint modeling. Theor. Appl. Climatol., 80, 121-41.CrossRef
  Schmid, H. P., 1994: Source areas for scalars and scalar fluxes. Bound.-Layer Meteor., 67, 293-18.CrossRef
  Schmid, H. P., 2002: Footprint modeling for vegetation atmosphe
• 作者单位：Hui Zhang (1)
  Xuefa Wen (2)
  
  1. Jinzhou Ecology and Agriculture Meteorological Center, Jinzhou, 121001, China
  2. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
  
• 刊物主题：Atmospheric Sciences; Meteorology; Geophysics and Environmental Physics; Atmospheric Protection/Air Quality Control/Air Pollution;
• 出版者：Springer Berlin Heidelberg
• ISSN：2191-4788

文摘

Spatial heterogeneity poses a major challenge for the appropriate interpretation of eddy covariance data. The quantification of footprint climatology is fundamental to improving our understanding of carbon budgets, assessing the quality of eddy covariance data, and upscaling the representativeness of a tower flux to regional or global scales. In this study, we elucidated the seasonal variation of flux footprint climatologies and the major factors that influence them using the analytical FSAM (Flux Source Area Model), KM (Kormann and Meixner, 2001), and H (Hsieh et al., 2000) models based on eddy covariance measurements at two and three times the canopy height at the Qianyanzhou site of ChinaFLUX in 2003. The differences in footprints among the three models resulted from different underlying theories used to construct the models. A comparison demonstrated that atmospheric stability was the main factor leading to differences among the three models. In neutral and stable conditions, the KM and FSAM values agreed with each other, but they were both lower than the H values. In unstable conditions, the agreement among the three models for rough surfaces was better than that for smooth surfaces, and the models showed greater agreement for a low measurement height than for a high measurement height. The seasonal flux footprint climatologies were asymmetrically distributed around the tower and corresponded well to the prevailing wind direction, which was north-northwest in winter and south-southeast in summer. The average sizes of the 90% flux footprint climatologies were 0.36-.74 and 1.5-.2 km2 at altitudes of two and three times the canopy height, respectively. The average sizes were ranked by season as follows: spring > summer > winter > autumn. The footprint climatology depended more on atmospheric stability on daily scale than on seasonal scale, and it increased with the increasing standard deviation of the lateral wind fluctuations. Keywords eddy covariance flux footprint flux footprint climatology model comparison