摘要
为了分析气候变化和大气CO2浓度增加对长白山阔叶红松林净初级生产力（NPP）的影响,运用本地参数化后的BIOME-BGC模型进行模拟,并以实测NPP和增强型植被指数（EVI）进行验证。模拟结果表明,长白山阔叶红松林NPP均值为611.71 gC/（m2·a）,1960—2011年年际间的波动范围是473.28~703.44 gC/（m2·a）。模拟结果与基于样地实测的NPP（均值为594.66 gC/（m2·a））相似;同时,BIOME-BGC模型模拟的NPP年际间变化趋势与EVI的波动趋势相似,二者间存在显著的相关关系,表明模型能较好地模拟生产力的时间动态。模拟表明,红松的NPP与降水关系更为密切,而阔叶树NPP与温度、降水都呈显著的正相关。模型预测,在未来CO2浓度加倍和温度、降水同时增加的场景下,长白山阔叶红松林NPP将显著增加,其中阔叶树和红松的NPP将分别增加27.87%和23.96%。单独增加温度（2℃）或单独增加降水（12%）都能促进阔叶树和红松NPP的增加,其中降水的作用弱于温度的作用,而单独CO2浓度的倍增对阔叶树和红松的NPP没有明显的影响。
In order to understand the response of net primary production （NPP） of broad-leaved Korean pine forest in Changbai Mountain to climate change and elevated atmospheric CO2, the authors simulated NPP with parameterized BIOME-BGC model and then tested with field-measured NPP and enhanced vegetation index （EVI）. The simulation results show that, the mean NPP of broad-leaved Korean pine forest in Changbai Mountain was 611.71 gC/（m2.a）, ranging between 473.28 and 703.44 gC/（m2.a）in 1960-2011. The model-simulated NPPs were in accordance with field NPP observations, and the temporal dynamics of simulated NPP was also consistent with that of EVI, suggesting a successful modeling of NPP patterns by BIOME-BGC. Simulated results indicate that the NPP of Korean pine （PK） was limited by growing season precipitation, while the NPP of deciduous broad-leaf trees （DB） was controlled by temperature and precipitation together. The BIOME-BGC predicts that the NPP of broad-leaved Korean pine forest will increasing remarkably under a scenario of increasing temperature, precipitation and atmospheric CO2 simultaneously, and the NPP of PK and DB will increase 23.96% and 27.87% respectively. Increasing temperature （2℃） or precipitation （12%） alone can both lead to increasing NPP of PK and DB, and temperature had stronger effect than precipitation. However, doubled CO2 alone do not have significant effect inincreasing NPP.
出处
《北京大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2014年第3期577-586,共10页
Acta Scientiarum Naturalium Universitatis Pekinensis
基金
国家"十二五"科技支撑计划(2012BAC01B03)
国家自然科学基金(31370620
31200146)资助
