Assessment of Uncertainties in Eddy Covariance Flux Measurement Based on Intensive Flux Matrix of HiWATER-MUSOEXE-文献浏览-A Big Earth Data Platform for Three Poles

Citation:
Wang, J.M., Zhuang, J.X., Wang, W.Z., Liu, S.M., Xu, Z.W. (2015). Assessment of uncertainties in eddy covariance flux measurement based on intensive flux matrix of HiWATER-MUSOEXE. IEEE Geoscience and Remote Sensing Letters, 12(2), 259-263. doi:10.1109/LGRS.2014.2334703.

Literature information
Title	Assessment of Uncertainties in Eddy Covariance Flux Measurement Based on Intensive Flux Matrix of HiWATER-MUSOEXE
Year	2015
Publisher	IEEE Geoscience and Remote Sensing Letters
Description	To study the multiscale characteristics of ecohydrological processes in the Heihe River Basin, an intensive flux observation matrix was established, which consisted of mainly 17 eddy covariance (EC) flux stations in a 5.5 km × 5.5 km area of the Zhangye oasis. Formal observations began in June and continued through September 2012. Before the main campaign, an intercomparison for all instruments (including 20 EC sets) was conducted in the Gobi desert. All the data provided a rare opportunity to assess the flux uncertainties of EC measurements. Three methods were chosen in this assessment. For the Gobi intercomparison, a simple method based on elementary error analysis could provide the systematic errors and random uncertainties for each EC; uncertainties for sensible heat flux were generally less than 10% in this area. For flux matrix observations, by using mainly the method of Mann and Lenschow (1994), the uncertainties estimated for sensible heat, latent heat, and CO2 fluxes were approximately 18%, 16%, and 21%, respectively, for the selected period. These were comparatively high because of the inherent heterogeneities of the oasis. The flux uncertainty quantification, including its probability distribution and the nonconstant variance characteristics shown for these data sets, is essential for flux data interpretation and applications, particularly the validation of relevant remote sensing models.
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