A new temperature-photoperiod coupled phenology module in LPJ-GUESS model v4.1: optimizing estimation of terrestrial carbon and water processes

作者：Shouzhi Chen, Yongshuo H. Fu, Mingwei Li, Zitong Jia, Yishuo Cui, Jing Tang · 年份：2023 · DOI：10.5194/gmd-2023-212 · 被引用次数：2 · 研究领域：Remote Sensing in Agriculture、Species Distribution and Climate Change、Land Use and Ecosystem Services

Abstract. Vegetation phenological shifts impact the terrestrial carbon and water cycle, and affects local climate system through biophysical and biochemical processes between biosphere and atmosphere. Dynamic Global Vegetation Models (DGVMs), serving as pivotal simulation tools for investigating terrestrial ecosystem carbon and water cycles, typically incorporate representations of vegetation phenological processes. Nevertheless, it is still a challenge to achieve accurate simulation of vegetation phenology in the DGVMs. Here, we developed and coupled the spring and autumn phenology models into one of the DGVMs, LPJ-GUESS. These process-based phenology models driven by temperature and photoperiod, and are parameterized for deciduous trees and shrubs using remote sensing-based phenological observations and reanalysis dataset ERA5 land. The results show that the developed LPJ-GUESS with new phenology modules substantially improved the accuracy in capturing start and end dates of growing seasons. For the start of growing season, the simulated RMSE for deciduous tree and shrubs decreased by 8.04 and 17.34, respectively. For the autumn phenology, the simulated RMSE for deciduous tree and shrubs decreased by 22.61 and 17.60, respectively. Interestingly, we have also found that differences in simulated start and end of growing season can largely alter the ecological niches and competitive relationships among different plant functional types (PFTs), and subsequentially impact the com...