Biogeosciences
www.biogeosciences.net
1726-4170
1726-4189
6
2
2009
10.5194/bg-6-251-2009
http://www.biogeosciences.net/6/251/2009/
http://www.biogeosciences.net/6/251/2009/bg-6-251-2009.html
http://www.biogeosciences.net/6/251/2009/bg-6-251-2009.pdf
251
266
2009-02-24
Drivers of inter-annual variability in Net Ecosystem Exchange in a semi-arid savanna ecosystem, South Africa
S. A. Archibald
sarchibald@csir.co.za
A. Kirton
M. R. van der Merwe
R. J. Scholes
C. A. Williams
N. Hanan
CSIR Natural Resources and Environment, PO Box 395, Pretoria 0001, South Africa
School of Geography, Clark University, MA, USA
Natural Resources Ecology Lab, Colorado State University, USA
Inter-annual variability in primary production and ecosystem respiration was
explored using eddy-covariance data at a semi-arid savanna site in the
Kruger Park, South Africa. New methods of extrapolating night-time
respiration to the entire day and filling gaps in eddy-covariance data in
semi-arid systems were developed. Net ecosystem exchange (NEE) in these
systems occurs as pulses associated with rainfall events, a pattern not
well-represented in current standard gap-filling procedures developed
primarily for temperate flux sites. They furthermore do not take into
account the decrease in respiration at high soil temperatures. An artificial
neural network (ANN) model incorporating these features predicted measured
fluxes accurately (MAE 0.42 gC/m<sup>2</sup>/day), and was able to represent the
seasonal patterns of photosynthesis and respiration at the site. The amount
of green leaf area (indexed using satellite-derived estimates of fractional
interception of photosynthetically active radiation <i>f</i><sub>APAR</sub>), and the
timing and magnitude of rainfall events, were the two most important
predictors used in the ANN model. These drivers were also identified by
multiple linear regressions (MLR), with strong interactive effects. The annual
integral of the filled NEE data was found to range from −138 to +155 g C/m<sup>2</sup>/y
over the 5 year eddy covariance measurement period. When applied
to a 25 year time series of meteorological data, the ANN model predicts an
annual mean NEE of 75(±105) g C/m<sup>2</sup>/y. The main correlates of this
inter-annual variability were found to be variation in the amount of
absorbed photosynthetically active radiation (APAR), length of the growing
season, and number of days in the year when moisture was available in the
soil.
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