研究利用基于冠层辐射传输与植物生理过程的MAESTRA模型,结合中国东部鼎湖山、千烟洲及长白山3个典型森林生态系统的CO2通量观测数据,对光合有效辐射（Photosynthetically Active Radiation,PAR）总量及其散射辐射比例变化影响下生态系统总初级生产力（Gross Primary Productivity,GPP）的变化进行了模拟与敏感性分析,从而探讨这两者的变化对森林生态系统GPP的综合影响。研究结果表明：PAR总量变化对GPP的影响程度由PAR总量变化幅度以及GPP对PAR总量变化的敏感程度所决定,较低的PAR总量与较高的温度条件下GPP对PAR总量变化较敏感;散射辐射比例增大可以提高森林冠层对入射PAR的吸收和利用效率,其对GPP的影响程度由散射辐射量的变化以及散射辐射与直射辐射在吸收与利用效率上的差别所决定,较高温度与叶面积条件下该差别较大;PAR总量与散射辐射比例共同变化对GPP的综合影响取决于上述两个过程的抵消结果,入射PAR较强时两者抵消作用通常更明显,在全年总量上,散射辐射比例变化对GPP的影响能抵消PAR总量变化影响的1/3~1/2。
Capacity of carbon sequestration in forest ecosystem largely depends on the trend of net primary production (NPP) and the length of ecosystem carbon residence time. Retrieving spatial patterns of ecosystem carbon residence time is important and necessary for accurately predicting regional carbon cycles in the future. In this study, a data-model fusion method that combined a process-based regional carbon model (TECO-R) with various ground-based ecosystem observations (NPP, biomass, and soil organic carbon) and auxiliary data sets (NDVI, meteorological data, and maps of vegetation and soil texture) was applied to estimate spatial patterns of ecosystem carbon residence time in Chinese forests at steady state. In the data-model fusion, the genetic algorithm was used to estimate the optimal model parameters related with the ecosystem carbon residence time by minimizing total deviation between modeled and observed values. The results indicated that data-model fusion technology could effectively retrieve model parameters and simulate carbon cycling processes for Chinese forest ecosystems. The estimated carbon residence times were highly heterogenous over China, with most of regions having values between 24 and 70 years. The deciduous needleleaf forest and the evergreen needleleaf forest had the highest averaged carbon residence times (73.8 and 71.3 years, respectively), the mixed forest and the deciduous broadleaf forest had moderate values (38.1 and 37.3 years, respectively), and the evergreen broadleaf forest had the lowest value (31.7 years). The averaged carbon residence time of forest ecosystems in China was 57.8 years.
ZHOU Tao1,2, SHI PeiJun1,2, JIA GenSuo3, LI XiuJuan1,2 & LUO YiQi4 1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
Temperature sensitivity of soil respiration (Q10) is an important parameter in modeling the effects of global warming on ecosystem carbon release. Experimental studies of soil respiration have ubiquitously indicated that Q10 has high spatial heterogeneity. However, most biogeochemical models still use a constant Q10 in projecting future climate change and no spatial pattern of Q10 values at large scales has been derived. In this study, we conducted an inverse modeling analysis to retrieve the spatial pattern of Q10 in China at 8 km spatial resolution by assimilating data of soil organic carbon into a proc-ess-based terrestrial carbon model (CASA model). The results indicate that the optimized Q10 values are spatially heterogeneous and consistent to the values derived from soil respiration observations. The mean Q10 values of different soil types range from 1.09 to 2.38, with the highest value in volcanic soil, and the lowest value in cold brown calcic soil. The spatial pattern of Q10 is related to environmental factors, especially precipitation and top soil organic carbon content. This study demonstrates that inverse modeling is a useful tool in deriving the spatial pattern of Q10 at large scales, with which being incorporated into biogeochemical models, uncertainty in the projection of future carbon dynamics could be potentially reduced.
ZHOU Tao1,2, SHI PeiJun1,2, HUI DaFeng3 & LUO YiQi4 1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
The global rate of fossil fuel combustion continues to rise, but the amount of CO2 accumulating in the atmosphere has not increased accordingly. The causes for this discrepancy are widely debated. Par- ticularly, the location and drivers for the interannual variability of atmospheric CO2 are highly uncertain. Here we examine links between global atmospheric CO2 growth rate (CGR) and the climate anomalies of biomes based on (1986―1995) global climate data of ten years and accompanying satellite data sets. Our results show that four biomes, the tropical rainforest, tropical savanna, C4 grassland and boreal forest, and their responses to climate anomalies, are the major climate-sensitive CO2 sinks/sources that control the CGR. The nature and magnitude by which these biomes respond to climate anomalies are generally not the same. However, one common influence did emerge from our analysis; the ex- tremely high CGR observed for the one extreme El Nio year was caused by the response of the tropical biomes (rainforest, savanna and C4 grassland) to temperature.
