基于叶物候改进的森林生态系统GPP的模拟研究

森林生态系统通过光合作用固定的碳（GPP）是陆地生态系统碳循环的重要组成部分，基于植被动态过程的陆地碳循环模型一直被认为是估算和预测森林GPP的主要工具。当前，由于此类模型对叶物候过程的模拟存在偏差，其森林GPP的模拟结果存在较大的不确定性。针对此问题，本项目拟采用热量与冷量累积的方法，结合物候相机观测、卫星遥感观测、以及气温数据，明确全球森林区域的物候参数，试图构建适用于全球尺度的叶物候模型；并进一步将该模型与基于植被动态的碳循环模型FORCCHN2进行耦合，以提高碳循环模型对森林GPP年内及年际的模拟效果，从而准确地量化全球变化背景下森林生态系统GPP的时空变化。研究结果有助于改进现有碳循环模型对森林GPP的模拟能力，并为评估与预测全球森林生态系统的叶物候与GPP提供科学依据。

Given that GPP products by photosynthesis of forest ecosystems are the important parts of terrestrial carbon sequestration, the terrestrial carbon cycle model based on the dynamic process of vegetation has always been considered as the essential tool for estimating and predicting forest GPP. Currently, the predicted error of leaf phenology leads to large uncertainties in the predictions of GPP by using these models. For this, this project plans to develop a global phenology model and the global phenology parameters by using the method of heat and chilling accumulation and by using the phenology camera observation data, remote sensing data, and temperature data. The phenology model is further coupled with FORCCHN2, a carbon cycle model based on the vegetation dynamics. The goals of this study are to improve both intra‐ and interannual predicted performance and to quantify forest GPP accurately under the background of global change. Our results could help to improve the simulated GPP performance of the existing carbon cycle models, as well as provide a basis for simulating and predicting dynamics of leaf phenology in global forests.