期刊
JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES
卷 117, 期 -, 页码 -出版社
AMER GEOPHYSICAL UNION
DOI: 10.1029/2010JG001407
关键词
-
资金
- North American Carbon Program
- Canadian Foundation for Climate and Atmospheric Sciences (CFCAS) [GR464]
- Canadian Carbon Program (CCP)
- Office of Science (BER), U.S. Department of Energy [DE-FG02-00ER63023]
Despite the wide acceptance of the big-leaf upscaling strategy in evapotranspiration modeling (e. g., the Penman-Monteith model), its usefulness in simulating canopy photosynthesis may be limited by the underlying assumption of homogeneous response of carbon assimilation light-response kinetics through the canopy. While previous studies have shown that the separation of the canopy into sunlit and shaded parts (i.e., two-leaf model) is typically more effective than big-leaf models for upscaling photosynthesis from leaf to canopy, a systematic comparison between these two upscaling strategies among multiple ecosystems has not been presented. In this study, gross primary productivity was modeled using two-leaf and big-leaf upscaling approaches in the Boreal Ecosystem Productivity Simulator for shrublands, broadleaf, and conifer forest types. When given the same leaf-level photosynthetic parameters, the big-leaf approach significantly underestimated canopy-level GPP while the two-leaf approach more closely predicted both the magnitude and day-to-day variability in eddy covariance measurements. The underestimation by the big-leaf approach is mostly caused by its exclusion of the photosynthetic contributions of shaded leaves. Tests of the model sensitivity to a foliage clumping index revealed that the contribution of shaded leaves to the total simulated productivity can be as high as 70% for highly clumped stands and seldom decreases below similar to 40% for less-clumped canopies. Our results indicate that accurate upscaling of photosynthesis across a broad array of ecosystems requires an accurate description of canopy structure in ecosystem models.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据