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土壤呼吸的温度敏感性(Q10值)是模拟全球变暖与生态系统碳释放之间反馈强度的重要参数.虽然实验研究表明Q10值具有明显的空间异质性,但由于其空间分布格局的定量数据的缺乏,目前绝大多数生物地球化学模型将其简化成一个常数,并以此来预测未来的气候变化,这在一定程度上增大了模型预测的不确定性.本研究基于土壤有机碳的实测数据,并结合碳循环过程模型(CASA模型),利用反演分析方法估算了8km空间分辨率下中国土壤呼吸温度敏感性的空间分布.结果表明,Q10值具有明显的空间异质性,且与实验方法估算的Q10值具有一致性;不同土壤类型的Q10值在1.09~2.38之间变化,其中火山灰土的Q10值最大,冷棕钙土的值最小;Q10值的空间分布与降水及土壤有机碳含量的关系密切.研究表明,该方法能有效反演Q10值的空间分布,从而有助于揭示碳循环规律并降低未来大气CO2浓度及气候变化预测的不确定性.
The temperature sensitivity of soil respiration (Q10 value) is an important parameter to simulate the feedback intensity between global warming and ecosystem carbon emissions.Although experimental studies have shown that the Q10 value has obvious spatial heterogeneity, due to its quantitative spatial distribution pattern At present, most of the biogeochemical models reduce it to a constant and predict the future climate change, which increases the uncertainty of the model prediction to a certain extent.This study based on the soil organic carbon The spatial distribution of soil respiration temperature sensitivity in China with 8 km spatial resolution was estimated by using the inversion analysis method.The results showed that the Q10 value had obvious spatial heterogeneity, Q10 values of different soil types varied from 1.09 to 2.38, of which the Q10 value of volcanic ash soil was the highest and the value of cold-brown soil was the lowest. The spatial distribution and precipitation of Q10 value and The results show that this method can effectively retrieve the spatial distribution of Q10 value, which can help reveal the carbon cycle and reduce the future atmospheric CO2 concentration Climate change prediction uncertainty.