Progress in plant phenology modeling under global climate change

作     者：Yongshuo FU Xinxi LI Xuancheng ZHOU Xiaojun GENG Yahui GUO Yaru ZHANG Yongshuo FU;Xinxi LI;Xuancheng ZHOU;Xiaojun GENG;Yahui GUO;Yaru ZHANG

作者机构：College of Water SciencesBeijing Normal UniversityBeijing 100875China 

出 版 物：《Science China Earth Sciences》 (中国科学（地球科学英文版）)

年 卷 期：2020年第63卷第9期

页      面：1237-1247页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0708[理学-地球物理学] 0706[理学-大气科学] 0704[理学-天文学] 0713[理学-生态学] 

基　　金：supported by the National Natural Science Foundation of China(Grant No.31770516) the National Key Research and Development Program of China(Grant No.2017YFA06036001) the 111 Project(Grant No.B18006) the Fundamental Research Funds for the Central Universities(Grant No.2018EYT05) 

主　　题：Global change Plant phenology Phenology modeling Machine learning Ecophysiological experiment 

摘      要：Plant phenology is the study of the timing of recurrent biological events and the causes of their timing with regard to biotic and abiotic *** phenology affects the structure and function of terrestrial ecosystems and determines vegetation feedback to the climate system by altering the carbon,water and energy fluxes between the vegetation and near-surface ***,an accurate simulation of plant phenology is essential to improve our understanding of the response of ecosystems to climate change and the carbon,water and energy balance of terrestrial *** studies have developed rapidly under global change conditions,while the research of phenology modeling is largely *** phenology modeling has become the primary limiting factor for the accurate simulation of terrestrial carbon and water *** the mechanism of phenological response to climate change and building process-based plant phenology models are thus important frontier *** this review,we first summarized the drivers of plant phenology and overviewed the development of plant phenology ***,we addressed the challenges in the development of plant phenology models and highlighted that coupling machine learning and Bayesian calibration into process-based models could be a potential approach to improve the accuracy of phenology simulation and prediction under future global change conditions.