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首页> 外文期刊>Canadian Journal of Forest Research >Sensitivity and uncertainty analysis of the carbon and water fluxes at the tree scale in Eucalyptus plantations using a metamodeling approach
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Sensitivity and uncertainty analysis of the carbon and water fluxes at the tree scale in Eucalyptus plantations using a metamodeling approach

机译:应用元模型方法对桉树人工林碳和水通量的敏感性和不确定性分析

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摘要

Understanding the consequences of changes in climatic and biological drivers on tree carbon and water fluxes is essential in forestry. Using a metamodeling approach, sensitivity and uncertainty analyses were carried out for a tree-scale model (MAESPA) to isolate the effects of climate, morphological and physiological traits, and intertree competition on the absorption of photosynthetically active radiation (APAR), gross primary production (GPP), transpiration (TR), light use efficiency (LUE), and water use efficiency (WUE) in clonal Eucalyptus plantations. The metamodel predicting daily TR was validated using one year of sap flow measurements and showed close agreement with the measurements (mean percentage error = 11%, n = 2155). Simulations showed that APAR, GPP, and TR were very sensitive to the tree morphology and to a competition index representing its local environment. LUE and WUE were, in addition, very sensitive to the natural variability of the physiological leaf and root parameters. A maximum percentage error of 10% in these parameters leads to 18%, 17%, 16%, 9%, and 18% uncertainty for APAR, GPP, TR, LUE, and WUE, respectively. The uncertainties in TR were highest for the smallest trees. This study highlighted the need to take account of the spatial and temporal variability of tree traits and environmental conditions for simulations at the tree scale.
机译:了解气候和生物驱动因素变化对树木碳和水通量的影响,对于林业至关重要。使用元模型方法,对树型模型(MAESPA)进行了敏感性和不确定性分析,以隔离气候,形态和生理特征以及树间竞争对光合有效辐射(APAR)吸收,初级生产总值的影响(GPP),蒸腾作用(TR),光利用效率(LUE)和水分利用效率(WUE)在桉树无性系人工林中。使用一年的树液流量测量来验证预测每日TR的元模型,并且该模型与测量结果密切相关(平均百分比误差= 11%,n = 2155)。仿真表明,APAR,GPP和TR对树的形态和代表其本地环境的竞争指数非常敏感。此外,LUE和WUE对生理叶片和根部参数的自然变异非常敏感。这些参数中的最大百分比误差10%分别导致APAR,GPP,TR,LUE和WUE的不确定度分别为18%,17%,16%,9%和18%。对于最小的树木,TR的不确定性最高。这项研究强调了在树规模上进行仿真时需要考虑树木特征的时空变化和环境条件。

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