JP1.1 Attributing Tall Tower Flux Data to Heterogeneous Vegetation

Monday, 2 August 2010
Castle Peak Ballroom (Keystone Resort)
Natascha Kljun, Swansea University, Swansea, United Kingdom; and G. Gelybó, Z. Barcza, A. Kern, and L. Haszpra

We present a method to spatially analyse tall tower eddy covariance measurements. The approach attributes the flux signal measured at 82 m height to the different ecosystems surrounding the heterogeneous tall tower site. For the identification of the ecosystems, remotely sensed vegetation index time series are used. Using 250 m grid resolution defined by the available MODIS vegetation index data, we quantify the spatial distribution of winter and summer crops around the tower site. The land cover classification scheme is then combined with a footprint climatology for 2003-2008 to quantify the spatial representativeness of the eddy covariance measurements.

Crop-specific NEE and GPP time series are calculated based on results of the footprint climatology and the classified CO2 flux measurements using light response curve methods (rectangular and non-rectangular hyperbola). The presented methodology is used to derive summer and winter crop specific CO2 exchange time series. The results show that within the footprint area, the relative contribution and spatial distribution of summer and winter crops is variable among the years, which may influence the measured signal due to the different timing of the intensive carbon uptake period and harvest. Indeed, the crop specific CO2 fluxes are markedly different for each year, and exhibit strong correlation with the crop specific NDVI time series. The reconstructed, crop specific NEE time series are compared to yield data from the region. The crop specific annual NEE sums reveal a good correlation with county scale winter wheat and maize yield (dominant winter and summer crops in the region). However, the results may be biased due to local climatic effects (especially local precipitation events) impacting local growth and hence tower measurements. These local effects would not be reflected in county scale yield statistics. Hence, the crop specific tower NEE are more representative for local conditions.

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