首页> 外文期刊>Agricultural and Forest Meteorology >Evaluation of the advanced canopy-atmosphere-soil algorithm (ACASA) model performance over Mediterranean maquis ecosystem.
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Evaluation of the advanced canopy-atmosphere-soil algorithm (ACASA) model performance over Mediterranean maquis ecosystem.

机译:在地中海马奎斯生态系统上评估先进的冠层-大气-土壤算法(ACASA)模型的性能。

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The Advanced Canopy-Atmosphere-Soil Algorithm (ACASA) model is used to predict energy, water and carbon fluxes over a Mediterranean maquis site located in North-Western Sardinia (Italy) and the model performance is evaluated. Flux simulations are compared with Eddy Covariance field measurements collected from 2004 to 2007. The site experiences a drought season during the summer months in which the vegetation becomes water stressed. Results from the months of January, April, and July are analyzed to demonstrate the model behavior in different environmental conditions. In general, simulated and observed fluxes matched when both the thermal and moisture regime are optimal. During the July water stress period the model underestimated latent heat and carbon fluxes due to a strong stress response linked to soil properties and plant physiological characteristics. The selection of values for key parameters, e.g. maximum ideal photosynthetic capacity (RUBISCO), wilting point, soil water content, and root and leaf area ratio, is crucial to obtain close agreement between simulated and observed fluxes. The model was designed so that the most sensitive parameters are measurable quantities. Using the ACASA model to predict energy and mass fluxes between the vegetation and atmosphere appears promising in this context, and it could significantly improve our ability to estimate fluxes for use in future studies.
机译:高级冠层-大气-土壤算法(ACASA)模型用于预测位于西北撒丁岛(意大利)的地中海水域的能量,水和碳通量,并对模型性能进行评估。将通量模拟与2004年至2007年收集的涡流协方差实地测量值进行比较。该站点在夏季经历干旱季节,其中植被变得缺水。分析了1月,4月和7月月份的结果,以证明模型在不同环境条件下的行为。通常,当热态和水分态均最佳时,模拟和观察到的通量匹配。在7月的水分胁迫期间,该模型低估了潜热和碳通量,这是由于与土壤特性和植物生理特性相关的强烈胁迫响应所致。选择关键参数的值,例如最大理想光合能力(RUBISCO),枯萎点,土壤含水量以及根与叶的面积比,对于在模拟通量和观测通量之间取得紧密一致至关重要。设计模型时,最敏感的参数是可测量的量。在这种情况下,使用ACASA模型预测植被和大气之间的能量通量和质量通量似乎很有希望,它可以显着提高我们估算通量的能力,以供将来研究使用。

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