An integrated phenology modelling framework in R
An integrated phenology modelling framework in R
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DOI：
10.1111/2041-210x.12970
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发表时间：
2018-05-01
影响因子：
6.6
通讯作者：
Richardson, Andrew D.
中科院分区：
文献类型：
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作者：
Hufkens, Koen;Basler, David;Richardson, Andrew D.
1.物候是一个一级控制生产力和介导的生物物理环境，通过改变水分，表面粗糙度长度和蒸散。因此，准确和透明的植被物候建模是理解生物圈和气候系统之间反馈的关键。在这里，我们介绍了PHENOR R包和建模框架。该框架利用了四个常见的物候观测数据集的植被物候测量，PhenoCam网络，美国国家物候网络（USA-NPN），泛欧物候项目（PEP 725），MODIS物候（MCD 12 Q2）结合（全球）回顾性和预测性气候数据。我们展示了一个例子分析，使用PHENOR建模框架，快速，轻松地比较了20个春季物候模型的三种植物功能类型。使用均方根（RMSE）误差对模型技能进行的分析显示，无论模型结构如何，差异很小或没有差异，这证实了之前的研究。我们认为，解决这个问题将需要新的模型开发结合简单的数据同化，如我们的框架所促进的。总之，我们希望R语言和统计计算环境中的PHENOR物候建模框架能够促进可重复性和社区驱动的物候模型开发，以提高其总体预测能力，并利用不断增长的物候数据产品。
1. Phenology is a first-order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and evapotranspiration. Accurate and transparent modelling of vegetation phenology is therefore key in understanding feedbacks between the biosphere and the climate system.2. Here, we present the PHENOR R package and modelling framework. The framework leverages measurements of vegetation phenology from four common phenology observation datasets, the PhenoCam network, the USA National Phenology Network (USA-NPN), the Pan European Phenology Project (PEP725), MODIS phenology (MCD12Q2) combined with (global) retrospective and projected climate data.3. We show an example analysis, using the PHENOR modelling framework, which quickly and easily compares 20 included spring phenology models for three plant functional types. An analysis of model skill using the root mean squared (RMSE) error shows little or no difference regardless of model structure, corroborating previous studies. We argue that addressing this issue will require novel model development combined with easy data assimilation as facilitated by our framework.4. In conclusion, we hope the PHENOR phenology modelling framework in the R language and environment for statistical computing will facilitate reproducibility and community driven phenology model development, in order to increase their overall predictive power, and leverage an ever growing number of phenology data products.