| 英文摘要： |
Assessing the terrestrial vegetation carbon density (TVCD) is crucial for evaluating the national carbon balance. However, current national-scale TVCD assessments show strong disparities, despite the good estimation method of their underlying models. Here, we attribute this contradiction to a flaw in the methods of using multimodel simulation results, which ignore the connections between results, leading to an overoptimistic evaluation of the multimodel ensemble mean (MMEM) method. Thus, using the state-of-the-art multimodel random forest ensemble (MMRFE) method to integrate the results of 10 models, we reproduced Chinese TVCD data during 1982-2010. Compared with the nationally averaged TVCD field investigation data (27 +/- 26 Mg C/ha), we found that the results of five models were overestimated by 7.4%-85.2%, and the remaining models were underestimated by 3.7%-77.8%. The MMEM TVCD method produced an overestimation of 2%, but the MMRFE method produced an underestimation of only 0.2%. Additionally, the summary Taylor diagrams of the TVCD at the national and ecosystem (forest, shrub, grass and crop ecosystems) scales all showed that the MMRFE TVCD produced the smallest standard deviations and root mean square deviations and the highest correlation coefficients. Furthermore, the MMRFE TVCDs were all significantly positively correlated with the normalized difference vegetation index (NDVI), and they had the same increasing trend, but an opposite variation trend from the MMEM TVCD and NDVI. This result implied that the spatiotemporal variation modes of the MMRFE TVCD were consistent with those of the NDVI. The results suggested that compared with the traditional MMEM method, the MMRFE TVCD and its spatiotemporal variation modes were more similar to the real TVCD. In conclusion, the MMRFE method can effectively improve the accuracy of national-scale TVCD estimation, and effectively reduce the uncertainty of large-scale terrestrial vegetation carbon estimation processes. Notably, we provide a new method that uses a machine learning approach to mine multimodel terrestrial carbon information to reduce the uncertainty in the estimation of terrestrial ecosystem carbon components. |
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