A steady-state approximation approach to simulate seasonal leaf dynamics of deciduous broadleaf forests via climate variables
A steady-state approximation approach to simulate seasonal leaf dynamics of deciduous broadleaf forests via climate variables
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DOI：
10.1016/j.agrformet.2017.11.025
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发表时间：
2018-02
影响因子：
6.2
通讯作者：
Q. Xin;Yongjiu Dai;Xia Li;Xiaoping Liu;P. Gong;A. Richardson
中科院分区：
文献类型：
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作者：
Q. Xin;Yongjiu Dai;Xia Li;Xiaoping Liu;P. Gong;A. Richardson
由于叶片是植物进行光合作用和蒸腾作用的基本要素，植被叶片动态控制着冠层的物理和生态地球化学过程，从而在很大程度上影响着陆面与大气之间的能量和物质交换。鉴于植物叶分配过程对气候和环境条件高度敏感，开发通过气候变量模拟叶动态的鲁棒模型是陆面模式和陆-气耦合模式的关键组成部分。在这里，我们提出了一种新的方法来模拟季节性叶片动态的基础上，应用植被生产力作为一个综合指标，跟踪和评估气候适应性，随着时间的推移，植物生长的想法。该方法首先通过迭代求解由生长生产日模型建立的叶片物候和冠层光合作用的闭合方程，得到稳态叶面积指数（LAI）的时间序列，然后采用简单移动平均法来考虑叶面积指数的滞后性。季节性叶面积指数模拟使用开发的方法同意从AmeriFlux网站的选择与现场测量所示的高决定系数（R2= 0.801）和低均方根误差（RMSE = 0.924平方米/平方米）和卫星衍生的数据（R2= 0.929和RMSE = 0.650平方米/平方米）的研究通量塔网站。此外，所提出的方法是能够模拟季节性叶面积指数的落叶阔叶林，在整个美国东部与卫星获得的叶面积指数的时间序列相匹配。比较建模研究表明，该方法产生更准确的结果比生长季节指数的方法的相关系数和误差度量。所开发的方法提供了一个完整的解决方案，建模季节性叶片动态以及冠层生产力单独使用气候变量。
As leaves are the basic elements of plants that conduct photosynthesis and transpiration, vegetation leaf dynamics controls canopy physical and biogeochemical processes and hence largely influences the interactive exchanges of energy and materials between the land surface and the atmosphere. Given that the processes of plant leaf allocation is highly sensitive to climatological and environmental conditions, developing robust models that simulate leaf dynamics via climate variables contributes a key component to land surface models and coupled land-atmosphere models. Here we propose a new method to simulate seasonal leaf dynamics based on the idea of applying vegetation productivity as a synthesized metric to track and assess the climate suitability to plant growth over time. The method first solves two closed simultaneous equations of leaf phenology and canopy photosynthesis as modeled using the Growing Production-Day model iteratively for deriving the time series of steady-state leaf area index (LAI), and then applies the method of simple moving average to account for the time lagging of leaf allocation behind steady-state LAI. The seasonal LAI simulated using the developed method agree with field measurements from a selection of AmeriFlux sites as indicated by high coefficient of determination (R2= 0.801) and low root mean square error (RMSE = 0.924 m2/m2) and with satellite-derived data (R2= 0.929 and RMSE = 0.650 m2/m2) for the studied flux tower sites. Moreover, the proposed method is able to simulate seasonal LAI of deciduous broadleaf forests that match with satellite-derived LAI time series across the entire eastern United States. Comparative modeling studies suggest that the proposed method produces more accurate results than the method based on Growing Season Index in terms of correlation coefficients and error metrics. The developed method provides a complete solution to modeling seasonal leaf dynamics as well as canopy productivity solely using climate variables.