Climate change and elevated atmospheric CO2 should affect the dynamics of soil organic carbon （SOC）. SOC dynamics under uncertain patterns of climate warming and elevated atmospheric CO2 as well as with different so...Climate change and elevated atmospheric CO2 should affect the dynamics of soil organic carbon （SOC）. SOC dynamics under uncertain patterns of climate warming and elevated atmospheric CO2 as well as with different soil erosion extents at Nelson Farm during 1998-100 were simulated using stochastic modelling. Results based on numerous simulations showed that SOC decreased with elevated atmospheric temperature but increased with atmospheric CO2 concentration. Therefore, there was a counteract effect on SOC dynamics between climate warming and elevated CO2. For different soil erosion extents, warming 1℃ and elevated atmospheric CO2 resulted in SOC increase at least 15%, while warming 5 ℃ and elevated CO2 resulted in SOC decrease more than 29%. SOC predictions with uncertainty assessment were conducted for different scenarios of soil erosion, climate change, and elevated CO2. Statistically, SOC decreased linearly with the probability. SOC also decreased with time and the degree of soil erosion. For example, in 2100 with a probability of 50%, SOC was 1 617, 1 167, and 892 g m^-2, respectively, for no, minimum, and maximum soil erosion. Under climate warming 5 ℃ and elevated CO2, the soil carbon pools became a carbon source to the atmosphere （P 〉 95%）. The results suggested that stochastic modelling could be a useful tool to predict future SOC dynamics under uncertain climate change and elevated CO2.展开更多
Forests have long life cycles of up to several hundred years and longer.They also have very different growth rates at different stages of their life cycles.Therefore the carbon cycle in forest ecosystems has long time...Forests have long life cycles of up to several hundred years and longer.They also have very different growth rates at different stages of their life cycles.Therefore the carbon cycle in forest ecosystems has long time scales,making it necessary to consider forest age in estimating the spatiotemporal dynamics of carbon sinks in forests.The focus of this article is to review methods for combining recent remote sensing data with historical climate data for estimating the forest carbon source and sink distribution.Satellite remote sensing provides useful data for the land surface in recent decades. The information derived from remote sensing data can be used for short-term forest growth estimation and for mapping forest stand age for longterm simulations.For short-term forest growth estimation, remote sensing can provide forest structural parameters as inputs to process-based models,including big-leaf,two-leaf,and multi-layered models. These models use different strategies to upscale from leaf to canopy,and their reliability and suitability for remote sensing applications will be examined here.For long-term forest carbon cycle estimation, the spatial distribution of the forest growth rate（net primary productivity,NPP） modeled using remote sensing data in recent years is a critical input.This input can be combined with a forest age map to simulate the historical variation of NPP under the influence of climate and atmospheric changes. Another important component of the forest carbon cycle is heterotrophic respiration in the soil,which depends on the sizes of soil carbon pools as well as climate conditions.Methods for estimating the soil carbon spatial distribution and its separation into pools are described.The emphasis is placed on how to derive the soil carbon pools from NPP estimation in current years with consideration of forest carbon dynamics associated with stand age variation and climate and atmospheric changes.The role of disturbance in the forest carbon cycle and the effects of forest regrowth after disturbance are also considered in this review.An example of national forest carbon budget estimation in Canada is given at the end.It illustrates the importance of forest stand age structure in estimating the national forest carbon budgets and the effects of climate and atmospheric changes on the forest carbon cycle.展开更多
基金Supported by the National Natural Science Foundation of China(Nos.51039007 and 51179212)the Fundamental Research Funds for the Central Universities
文摘Climate change and elevated atmospheric CO2 should affect the dynamics of soil organic carbon （SOC）. SOC dynamics under uncertain patterns of climate warming and elevated atmospheric CO2 as well as with different soil erosion extents at Nelson Farm during 1998-100 were simulated using stochastic modelling. Results based on numerous simulations showed that SOC decreased with elevated atmospheric temperature but increased with atmospheric CO2 concentration. Therefore, there was a counteract effect on SOC dynamics between climate warming and elevated CO2. For different soil erosion extents, warming 1℃ and elevated atmospheric CO2 resulted in SOC increase at least 15%, while warming 5 ℃ and elevated CO2 resulted in SOC decrease more than 29%. SOC predictions with uncertainty assessment were conducted for different scenarios of soil erosion, climate change, and elevated CO2. Statistically, SOC decreased linearly with the probability. SOC also decreased with time and the degree of soil erosion. For example, in 2100 with a probability of 50%, SOC was 1 617, 1 167, and 892 g m^-2, respectively, for no, minimum, and maximum soil erosion. Under climate warming 5 ℃ and elevated CO2, the soil carbon pools became a carbon source to the atmosphere （P 〉 95%）. The results suggested that stochastic modelling could be a useful tool to predict future SOC dynamics under uncertain climate change and elevated CO2.
文摘Forests have long life cycles of up to several hundred years and longer.They also have very different growth rates at different stages of their life cycles.Therefore the carbon cycle in forest ecosystems has long time scales,making it necessary to consider forest age in estimating the spatiotemporal dynamics of carbon sinks in forests.The focus of this article is to review methods for combining recent remote sensing data with historical climate data for estimating the forest carbon source and sink distribution.Satellite remote sensing provides useful data for the land surface in recent decades. The information derived from remote sensing data can be used for short-term forest growth estimation and for mapping forest stand age for longterm simulations.For short-term forest growth estimation, remote sensing can provide forest structural parameters as inputs to process-based models,including big-leaf,two-leaf,and multi-layered models. These models use different strategies to upscale from leaf to canopy,and their reliability and suitability for remote sensing applications will be examined here.For long-term forest carbon cycle estimation, the spatial distribution of the forest growth rate（net primary productivity,NPP） modeled using remote sensing data in recent years is a critical input.This input can be combined with a forest age map to simulate the historical variation of NPP under the influence of climate and atmospheric changes. Another important component of the forest carbon cycle is heterotrophic respiration in the soil,which depends on the sizes of soil carbon pools as well as climate conditions.Methods for estimating the soil carbon spatial distribution and its separation into pools are described.The emphasis is placed on how to derive the soil carbon pools from NPP estimation in current years with consideration of forest carbon dynamics associated with stand age variation and climate and atmospheric changes.The role of disturbance in the forest carbon cycle and the effects of forest regrowth after disturbance are also considered in this review.An example of national forest carbon budget estimation in Canada is given at the end.It illustrates the importance of forest stand age structure in estimating the national forest carbon budgets and the effects of climate and atmospheric changes on the forest carbon cycle.
