Field assessments on impact of CO2 concentration fluctuations along with complex terrain flows on the estimation of the net ecosystem exchange of temperate forests
Abstract. The CO2 storage (Fs) is the cumulation or depletion in CO2 amount over a period in an ecosystem. Along with the eddy-covariance flux and wind-stream advection of CO2, it is a major term in the net ecosystem CO2 exchange (NEE) equation, dominating under stable atmospheric stratification for forest ecosystems over complex terrains. However, estimating the Fs remains challenging due to the frequent gusts and random fluctuations in boundary-layer flows, posing difficulties in capturing the true trend of CO2 changes for accurate storage estimation using eddy-covariance along with the atmospheric profile techniques. Using the measurements from Qingyuan Ker Towers equipped with NEE instrument systems separately covering mixed-broadleaf, oak, and larch forests in a mountain watershed, this study investigates the gust periods and CO2 fluctuation magnitudes while examining their impact on Fs estimation in relation to the terrain complexity index (TCI). The gusts induce CO2 fluctuations at numerous periods of 1 to 10 min over two hours. Diurnal, seasonal, and spatial differences (P < 0.01) in the maximum amplitude of CO2 fluctuations (Amax) ranges from 1.6 to 136.7 ppm and these difference in a period (Pmax) at the same significant level ranges 140 to 170 second. The Amax and Pmax are significantly correlated to the magnitude and random error of Fs with diurnal and seasonal differences. These correlations decrease as CO2 averaging time windows becomes longer. To minimize the uncertainties of Fs, a constant [CO2] averaging time window for the Fs estimates is not ideal. Dynamic averaging time windows and a decision-level fusion model can reduce the potential Fs underestimation by 28.6 %–33.3 %, being equivalent to 1.9 %–4.3 % NEE underestimation for temperate forests. The study’s approach is notable for incorporating TCI and utilizing three flux towers for replication, making the findings applicable to similar regions with a single tower.
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