青藏高原草地生态系统生物量碳库研究进展

Review of Researches on Biomass Carbon Stock in Grassland Ecosystem of Qinghai-Tibetan Plateau

准确认识青藏高原草地生物量碳库及其变化规律对研究区域碳循环与合理利用草地资源具有重要意义。通过综述相关文献,将青藏高原草地生物量碳库的研究方法与结果概括如下:①当前草地生物量碳库的估算方法主要有4种:文献记录法、实地调查法、遥感—植被指数法和过程模型法等,而估算方法、采样标准与地下生物量估算是导致结果差异的主要因素;②文献结果显示,青藏高原草地生物量的平均碳密度为223 g/m2,碳库为277TgC(1Tg=1012g);③采用遥感的估算结果表明,近20年来青藏高原草地地上生物量碳库呈增加的趋势,总体处于碳汇状态;④影响青藏高原草地地上生物量的主要因子是降水量,温度对生物量的影响还存在不确定性,人为干扰也是影响草地生物量的重要因素。目前青藏高原草地生物量碳库的研究仍存在着一些问题,应从基础观测数据、遥感模型算法与碳—氮—水耦合过程模拟等方面开展更为深入的研究。

It is critical to know Qinghai-Tibetan Plateau＇ s grassland biomass carbon（C） stock and its dynamics in order to study the regional C cycle and sustainable use of grassland resources. After reviewing the publications, the authors present a summary of methods and results in the studies of biomass C stock in grassland ecosystem of Qinghai-Tibetan Plateau. （1） Four methods are mainly used in this field： searching in literature and documents, field measurement, remote sensing of vegetation/vegetation indices, and process modeling. In the practice, methods of estimation, quality standards for sample collection, and underground biomass estimation are the most important factors impacting the results. （2） According to the published literature, biomass C density of Qinghai-Tibetan Plateau＇ s grasslands is approximately 223g/m^2, and can be translated to a total grassland bio- mass C stock of 277 Tg C （1Tg=10^12g）. （3） The estimation results based on remote sensing indicate that the biomass C stock of Qinghai-Tibetan Plateau＇ s grasslands increased over the past 20 years, suggesting that alpine grasslands might have functioned as a biomass C sink. （4） The above ground biomass C stock of Qinghai-Tibetan Plateau＇ s grasslands is strongly affected by precipitations, while the role of temperature is unclear. In addi- tion, human activities are considered to be a crucial factor affecting grassland biomass C stock as well. Problems remain in the studies of biomass C stock in grassland ecosystem of Qinghai-Tibetan Plateau; more thorough in- vestigations are needed in the fields such as data acquirement in the basic field measurements, optimization of algorithms for remotely-sensed vegetation indices, and process modeling of carbon- nitrogen- water coupling cy- cle in the alpine ecosystem.