Uncertainty analysis in data processing on the estimation of net carbon exchanges at different forest ecosystems in China

Abstract

Information about the uncertainties associated with eddy covariance observations of surface-atmosphere CO₂ exchange is of importance for model-data fusion in carbon cycling studies and the accurate evaluation of ecosystem carbon budgeting. In this paper, a comprehensive analysis was conducted to investigate the influence of data processing procedures, focusing especially on the nocturnal data correction and three procedures in nonlinear regression method of gap filling [i.e., the selection of respiration model (REM), light-response model (LRM) and parameter optimization criteria (POC)], on the annual net ecosystem CO₂ exchange estimation at three forest ecosystems in ChinaFLUX with three yearly datasets for each site. The results showed that uncertainties caused from four methodological uncertainties were between 61 and 108…⁻² year⁻¹, with 61–93…⁻² year⁻¹ (21–30%) in a temperate mixed forest, 80–107…⁻² year⁻¹ (19–21%) in a subtropical evergreen coniferous plantation and 77–108…⁻² year⁻¹ (16–19%) in a subtropical evergreen broad-leaved forest. Factorial analysis indicated that the largest uncertainty was associated with the choice of POC in the regression method across all sites in all years, while the influences of the choice of models (i.e., REM and LRM) varied with climate conditions at the measurement station. Furthermore, the uncertainty caused by data processing procedures was of approximately the same magnitude as the interannual variability in the three sites. This result stressed the importance to understand the uncertainty caused by data processing to avoid the introduction of artificial between-year and between-site variability that hampers comparative analysis.

Keywords:

• ChinaFLUX
• Data processing
• Eddy covariance
• Net carbon exchange
• Uncertainty

Acknowledgments

This research is supported by the National Key Basic Research and Development Program (Grant No. 2010CB833503), Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCM1-SW-01-01A) and the Chinese Academy of Sciences Directionality Project (Grant No. KZCX2-YW-433-06). The authors are grateful to the staffs of the Changbaishan (CBS), Qianyanzhou (QYZ), and Dinghushan (DHS) research station for providing observation data. The helpful and constructive suggestions and comments by the two anonymous reviewers are also gratefully acknowledged.