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A Semiprognostic Phenology Model for Simulating Multidecadal Dynamics of Global Vegetation Leaf Area Index
Journal of Advances in Modeling Earth Systems ( IF 4.4 ) Pub Date : 2020-06-24 , DOI: 10.1029/2019ms001935
Qinchuan Xin 1, 2 , Xuewen Zhou 3 , Nan Wei 3 , Hua Yuan 3 , Zurui Ao 1 , Yongjiu Dai 3
Affiliation  

Vegetation leaf phenology, often reflected by the dynamics in leaf area index (LAI), influences a variety of land surface processes. Robust models of vegetation phenology are pivot components in both land surface models and dynamic global vegetation models but remain challenging in terms of the model accuracy. This study develops a semiprognostic phenology model that is suitable for simulating time series of vegetation LAI. This method establishes a linear relationship between the steady‐state LAI (i.e., the LAI when the environment conditions remain unchanging) and gross primary productivity, meaning that the LAI an unchanging environment can carry is proportional to the photosynthetic products produced by plant leaves and implements with a simple light use efficiency algorithm of MOD17 to form a closed set of equations. We derive an analytical solution based on the Lambert W function to the closed equations and then apply a simple restricted growth process model to simulate the time series of actual LAI. The results modeled using global climate data demonstrate that the model is able to capture both the spatial pattern and intra‐annual and interannual variation of LAI derived from the satellite‐based product on a global scale. The results modeled using the flux tower data suggest that the developed model is able to explain over 70% variation in daily LAI for each plant functional type except evergreen broadleaf forest. The developed semiprognostic approach provides a simple solution to modeling the spatiotemporal variation in vegetation LAI across plant functional types on the global scale.

中文翻译:

用于模拟全球植被叶面积指数的多年代动态的半预测物候模型

植被叶片物候学通常反映在叶片面积指数(LAI)的动态变化中,它影响着多种土地表面过程。稳健的植被物候模型是陆地表面模型和动态全局植被模型的关键组成部分,但在模型准确性方面仍然具有挑战性。这项研究建立了一个半预言物候模型,适用于模拟植被LAI的时间序列。此方法在稳态LAI(即,环境条件保持不变时的LAI)与总初级生产力之间建立线性关系,这意味着不变的环境可以携带的LAI与植物叶片和工具产生的光合产物成比例用MOD17的简单光利用效率算法形成一个封闭的方程组。我们导出基于Lambert W函数的闭合方程的解析解,然后应用简单的受限增长过程模型来模拟实际LAI的时间序列。使用全球气候数据建模的结果表明,该模型能够捕获全球范围内源自卫星产品的LAI的空间格局以及年内和年际变化。使用流量塔数据建模的结果表明,所开发的模型能够解释除常绿阔叶林以外的每种植物功能类型的每日LAI的70%以上变化。所开发的半预测方法提供了一种简单的解决方案,可以在全球范围内对跨植物功能类型的植被LAI的时空变化建模。
更新日期:2020-06-24
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