To predict global climate change and to implement the Kyoto Protocol for stabilizing atmospheric greenhouse gases concentrations require quantifying spatio-temporal variations in the terrestrial carbon sink accurately...To predict global climate change and to implement the Kyoto Protocol for stabilizing atmospheric greenhouse gases concentrations require quantifying spatio-temporal variations in the terrestrial carbon sink accurately. During the past decade multi-scale ecological experiment and observation networks have been established using various new technologies (e.g. controlled environmental facilities, eddy covariance techniques and quantitative remote sensing), and have obtained a large amount of data about terrestrial ecosystem carbon cycle. However, uncertainties in the magnitude and spatio-temporal variations of the terrestrial carbon sink and in understanding the underlying mechanisms have not been reduced significantly. One of the major reasons is that the observations and experiments were conducted at individual scales independently, but it is the interactions of factors and processes at different scales that determine the dynamics of the terrestrial carbon sink. Since experiments and observations are always conducted at specific scales, to understand cross-scale interactions requires mechanistic analysis that is best to be achieved by mechanistic modeling. However, mechanistic ecosystem models are mainly based on data from single-scale experiments and observations and hence have no capacity to simulate mechanistic cross-scale interconnection and interactions of ecosystem processes. New-generation mechanistic ecosystem models based on new ecological theoretical framework are needed to quantify the mechanisms from micro-level fast eco-physiological responses to macro-level slow acclimation in the pattern and structure in disturbed ecosystems. Multi-scale data-model fusion is a recently emerging approach to assimilate multi-scale observational data into mechanistic, dynamic modeling, in which the structure and parameters of mechanistic models for simulating cross-scale interactions are optimized using multi-scale observational data. The models are validated and evaluated at different spatial and temporal scales and real-time observational data are assimilated continuously into dynamic modeling for predicting and forecasting ecosystem changes realistically. in summary, a breakthrough in terrestrial carbon sink research requires using approaches of multi-scale observations and cross-scale modeling to understand and quantify interconnections and interactions among ecosystem processes at different scales and their controls over ecosystem carbon cycle.展开更多
The net primary productivity of vegetation reflects the total amount of carbon fixed by plants through photosynthesis each year. The study of vegetation net primary productivity is one of the core contents of global c...The net primary productivity of vegetation reflects the total amount of carbon fixed by plants through photosynthesis each year. The study of vegetation net primary productivity is one of the core contents of global change and terrestrial ecosystems. This article reviews the current research status of net primary productivity of terrestrial vegetation, and comprehensively analyzes the advantages and disadvantages of three types of productivity estimation models, climate relative models, biogeochemical models, and light energy utilization models. The light energy utilization models have become the mainstream method for estimating vegetation net primary productivity because they can directly use remote sensing data. However, there are still many deficiencies in the estimation of vegetation net primary productivity, which need to be further improved and tested.展开更多
人类社会从陆地生态系统获取生产和生活资料的同时也作为一种干扰形式改变着地气之间的动态平衡。这三个既独立又相互耦合的子系统共同组成了一个复杂的陆地系统。如何深入理解这一系统的过程和机制是人类应对气候变化挑战的前提条件。...人类社会从陆地生态系统获取生产和生活资料的同时也作为一种干扰形式改变着地气之间的动态平衡。这三个既独立又相互耦合的子系统共同组成了一个复杂的陆地系统。如何深入理解这一系统的过程和机制是人类应对气候变化挑战的前提条件。陆地生态系统模型作为一种集成工具,已广泛应用于全球变化研究的各个领域,但从输入数据到模型结构和过程等诸多方面仍存在很大的不确定性。近年来,随着大气和地面生态观测网络的不断完善以及遥感等空间技术的不断强大,使陆地生态系统模型进一步发展和突破成为可能。新一代多因子驱动的陆地生态系统动态模型(DynamicL and Ecosystem Model,DLEM)正是在这一背景下应运而生的。本文旨在介绍DLEM的主体框架、输入输出变量、关键过程、主要功能和特点。展开更多
基金This study was supported by the China's Ministry of Science and Technology(Grant No.G2002CB412507)the National Natural Science Foundation of China(Grant No.40425103).
文摘To predict global climate change and to implement the Kyoto Protocol for stabilizing atmospheric greenhouse gases concentrations require quantifying spatio-temporal variations in the terrestrial carbon sink accurately. During the past decade multi-scale ecological experiment and observation networks have been established using various new technologies (e.g. controlled environmental facilities, eddy covariance techniques and quantitative remote sensing), and have obtained a large amount of data about terrestrial ecosystem carbon cycle. However, uncertainties in the magnitude and spatio-temporal variations of the terrestrial carbon sink and in understanding the underlying mechanisms have not been reduced significantly. One of the major reasons is that the observations and experiments were conducted at individual scales independently, but it is the interactions of factors and processes at different scales that determine the dynamics of the terrestrial carbon sink. Since experiments and observations are always conducted at specific scales, to understand cross-scale interactions requires mechanistic analysis that is best to be achieved by mechanistic modeling. However, mechanistic ecosystem models are mainly based on data from single-scale experiments and observations and hence have no capacity to simulate mechanistic cross-scale interconnection and interactions of ecosystem processes. New-generation mechanistic ecosystem models based on new ecological theoretical framework are needed to quantify the mechanisms from micro-level fast eco-physiological responses to macro-level slow acclimation in the pattern and structure in disturbed ecosystems. Multi-scale data-model fusion is a recently emerging approach to assimilate multi-scale observational data into mechanistic, dynamic modeling, in which the structure and parameters of mechanistic models for simulating cross-scale interactions are optimized using multi-scale observational data. The models are validated and evaluated at different spatial and temporal scales and real-time observational data are assimilated continuously into dynamic modeling for predicting and forecasting ecosystem changes realistically. in summary, a breakthrough in terrestrial carbon sink research requires using approaches of multi-scale observations and cross-scale modeling to understand and quantify interconnections and interactions among ecosystem processes at different scales and their controls over ecosystem carbon cycle.
文摘The net primary productivity of vegetation reflects the total amount of carbon fixed by plants through photosynthesis each year. The study of vegetation net primary productivity is one of the core contents of global change and terrestrial ecosystems. This article reviews the current research status of net primary productivity of terrestrial vegetation, and comprehensively analyzes the advantages and disadvantages of three types of productivity estimation models, climate relative models, biogeochemical models, and light energy utilization models. The light energy utilization models have become the mainstream method for estimating vegetation net primary productivity because they can directly use remote sensing data. However, there are still many deficiencies in the estimation of vegetation net primary productivity, which need to be further improved and tested.
文摘人类社会从陆地生态系统获取生产和生活资料的同时也作为一种干扰形式改变着地气之间的动态平衡。这三个既独立又相互耦合的子系统共同组成了一个复杂的陆地系统。如何深入理解这一系统的过程和机制是人类应对气候变化挑战的前提条件。陆地生态系统模型作为一种集成工具,已广泛应用于全球变化研究的各个领域,但从输入数据到模型结构和过程等诸多方面仍存在很大的不确定性。近年来,随着大气和地面生态观测网络的不断完善以及遥感等空间技术的不断强大,使陆地生态系统模型进一步发展和突破成为可能。新一代多因子驱动的陆地生态系统动态模型(DynamicL and Ecosystem Model,DLEM)正是在这一背景下应运而生的。本文旨在介绍DLEM的主体框架、输入输出变量、关键过程、主要功能和特点。
文摘准确估算陆地总初级生产力GPP(Gross Primary Productivity)数值对碳循环过程模拟有重要影响。本文介绍了多种基于植被指数以及基于光能利用率的遥感GPP算法,综述了不同算法在其研究区域的估算精度;并分析了MODIS/GPP以及BESS/GPP两种遥感GPP产品在不同植被类型的估算精度。通过对比全球碳通量站网络GPP数据表明,MODIS/GPP产品在全球估算结果具显著相关性(R2=0.59)及中等标准误差(RMSE=2.86 g C/m2/day),估算精度较高的植被类型有落叶阔叶林,草地等;估算精度较低类型包括常绿阔叶林,稀树草原等。本文对GPP产品中存在的不确定性进行分析,通过综述前人研究中发现的遥感估算GPP方法中存在的问题,指出可能的提高卫星遥感GPP产品估算精度的方法及发展趋势。
