Biogeosciences, 10, 2193-2217, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
03 Apr 2013
Spatially explicit regionalization of airborne flux measurements using environmental response functions
S. Metzger1,2,*,**, W. Junkermann1, M. Mauder1, K. Butterbach-Bahl1, B. Trancón y Widemann3,6,***, F. Neidl1, K. Schäfer1, S. Wieneke4, X. H. Zheng2, H. P. Schmid1, and T. Foken5,6 1Karlsruhe Institute of Technology, Institute for Meteorology and Climate Research, Atmospheric Environmental Research Division, Garmisch-Partenkirchen, Germany
2Chinese Academy of Sciences, Institute of Atmospheric Physics, State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Beijing, China
3University of Bayreuth, Chair of Ecological Modelling, Bayreuth, Germany
4University of Cologne, Institute of Geophysics and Meteorology, Cologne, Germany
5University of Bayreuth, Department of Micrometeorology, Bayreuth, Germany
6Member of Bayreuth Centre of Ecology and Ecosystem Research, Bayreuth, Germany
*now at: National Ecological Observatory Network, Fundamental Instrument Unit, Boulder, USA
**now at: University of Colorado, Institute of Arctic and Alpine Research, Boulder, USA
***now at: Technical University of Ilmenau, Programming Languages and Compilers, Ilmenau, Germany
Abstract. The goal of this study is to characterize the sensible (H) and latent (LE) heat exchange for different land covers in the heterogeneous steppe landscape of the Xilin River catchment, Inner Mongolia, China. Eddy-covariance flux measurements at 50–100 m above ground were conducted in July 2009 using a weight-shift microlight aircraft. Wavelet decomposition of the turbulence data enables a spatial discretization of 90 m of the flux measurements. For a total of 8446 flux observations during 12 flights, MODIS land surface temperature (LST) and enhanced vegetation index (EVI) in each flux footprint are determined. Boosted regression trees are then used to infer an environmental response function (ERF) between all flux observations (H, LE) and biophysical (LST, EVI) and meteorological drivers. Numerical tests show that ERF predictions covering the entire Xilin River catchment (≈3670 km2) are accurate to ≤18% (1 σ). The predictions are then summarized for each land cover type, providing individual estimates of source strength (36 W m−2 < H < 364 W m−2, 46 W m−2 < LE < 425 W m−2) and spatial variability (11 W m−2 < σH < 169 W m−2, 14 W m−2 < σLE < 152 W m−2) to a precision of ≤5%. Lastly, ERF predictions of land cover specific Bowen ratios are compared between subsequent flights at different locations in the Xilin River catchment. Agreement of the land cover specific Bowen ratios to within 12 ± 9% emphasizes the robustness of the presented approach. This study indicates the potential of ERFs for (i) extending airborne flux measurements to the catchment scale, (ii) assessing the spatial representativeness of long-term tower flux measurements, and (iii) designing, constraining and evaluating flux algorithms for remote sensing and numerical modelling applications.

Citation: Metzger, S., Junkermann, W., Mauder, M., Butterbach-Bahl, K., Trancón y Widemann, B., Neidl, F., Schäfer, K., Wieneke, S., Zheng, X. H., Schmid, H. P., and Foken, T.: Spatially explicit regionalization of airborne flux measurements using environmental response functions, Biogeosciences, 10, 2193-2217,, 2013.
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