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毛嘉富, 王斌, 戴永久, 等. 一个动态植被模型在欧洲森林碳水循环模拟中的适应性评估研究[J]. 大气科学, 2008, 32(6): 1379-1391. DOI: 10.3878/j.issn.1006-9895.2008.06.12
引用本文: 毛嘉富, 王斌, 戴永久, 等. 一个动态植被模型在欧洲森林碳水循环模拟中的适应性评估研究[J]. 大气科学, 2008, 32(6): 1379-1391. DOI: 10.3878/j.issn.1006-9895.2008.06.12
Mao Jiafu, Wang Bin, Dai Yongjiu, et al. Simulation and evaluation of carbon and water fluxes at 15 Europe forest sites with a dynamic global vegetation model[J]. Chinese Journal of Atmospheric Sciences, 2008, 32(6): 1379-1391. DOI: 10.3878/j.issn.1006-9895.2008.06.12
Citation: Mao Jiafu, Wang Bin, Dai Yongjiu, et al. Simulation and evaluation of carbon and water fluxes at 15 Europe forest sites with a dynamic global vegetation model[J]. Chinese Journal of Atmospheric Sciences, 2008, 32(6): 1379-1391. DOI: 10.3878/j.issn.1006-9895.2008.06.12

一个动态植被模型在欧洲森林碳水循环模拟中的适应性评估研究

Simulation and evaluation of carbon and water fluxes at 15 Europe forest sites with a dynamic global vegetation model

  • 摘要: 对动态全球植被模型M-SDGVM (Modified Sheffield Dynamic Global Vegetation Model), 在1996~1998年15个欧洲森林通量站碳通量和水汽通量的季节和年际变化进行模拟和评估研究, 总的来说, 模型能够合理再现各个站点春、 夏季节碳的吸收, 秋、 冬季节碳的释放, 以及水汽释放的季节变化趋势, 其中, 对水汽通量的模拟更为理想。对模型的上述适应性评估研究表明, 改进后的M-SDGVM有能力研究不同气候条件下欧洲森林生态系统碳、 水循环过程及其响应机制, 但是, 模型对部分站点的模拟仍存在不确定性, 通过对这些偏差及其可能的产生机理进行分析, 有助于模型的进一步发展和应用研究。

     

    Abstract: The M-SDGVM was evaluated against the eddy-covariance measurements of seasonal and annual carbon and water fluxes at 15 forest sites within the EUROFLUX project from 1996 to 1998. Generally, the M-SDGVM can reasonably reproduce the seasonal trend of carbon and water exchange among different sites. It correctly simulated the net uptake of carbon during spring and summer months and the release of carbon during autumn and winter months. And the M-SDGVM also did a better job in predicting the release of water to the atmosphere during all the seasons. The results shown here suggest that in general the M-SDGVM can be used as a useful tool for studies of climate change impacts on carbon and water cycles of forests in Europe and their possible feedbacks. However, the model shows some biases at part of the sites. Analyzing and quantifying the case studies with poor performance will lead to further insights on modifications and improvements of the current version of M-SDGVM.

     

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