Towards a better model representation of vegetation autumn phenology of temperate -zone deciduous trees.

Autumn phenological events (e.g. leaf senescence) signal the end of vegetation activity in deciduous trees and alter their albedo, thereby exerting a strong control on various ecological processes and climate feedbacks. Predicting their timing with high accuracy is a prerequisite for better understanding the climate-ecosystem interactions. Modeling autumn phenology at larger spatial and temporal scales remains challenging, because the processes behind autumn phenological events are not well understood. Previous experimental studies have not resulted in a strong consensus on the relationship between environmental cues and leaf senescence. Most of current phenological models regarded temperature and/or photoperiod sum as the primary predictors, but have neglected the impact of other, recently discovered cues, such as nutrient limitation and drought extremes. In this project, the applicant seeks to: i) set up a database covering the records extracted from phenological observation networks as well as metrics derived from eddy covariance and remote sensing-based measurement. ii) evaluate current models at multiple spatial scales. iii) develop a new mechanistic/semimechanistic model that considers recently discovered environmental cues and allows improved model structures. The applicant will also couple this newly developed phenology model with a state-of-the art dynamic global vegetation model to improve its predictive capacity of ecosystem carbon balances.

Keywords:PLANT ECOLOGY

Disciplines:Climatology, Remote sensing, Global ecology, Plant ecology