植被光合呼吸模型在千烟洲亚热带常绿针叶林的优化及验证

Optimization and evaluation of vegetation photosynthesis and respiration model using the measurements collected from the forest site of subtropical coniferous-evergreen

碳循环模型参数的确定和优化对生态系统净CO2交换(NEE)的模型计算至关重要。该文利用2010–2012年China FLUX千烟洲站点的通量观测资料,对植被光合呼吸模型(VPRM)的参数进行了优化。通过比较两种不同的拟合方案,发现利用传统光响应方程得到的参数不适用于VPRM,而利用模型自身反演方案拟合得到的参数最大光量子效率(λ)达0.203,大于C3植物平均值,但与其他相关研究结果吻合。采用VPRM模型反演方案优化得到的参数后,VPRM能较准确地模拟千烟洲站不同季节的NEE。其对全年半小时NEE模拟的平均误差为–0.86μmol·m–2·s–1,相关系数为0.72。模型可准确地模拟生长旺季NEE平均日变化,但低估了非生长旺季白天吸收峰值约52%。通过个例分析发现,VPRM模型可以准确模拟晴天条件下NEE的时间变化,但对阴雨天条件下NEE的模拟还存在较大的不确定性。该研究将有助于进一步改进CO2通量及浓度的区域数值模拟。

Aims Determination of carbon cycling model parameters is critical to simulate the net ecosystem CO2 exchange （NEE）. The objectives of this study were to determine the parameters of vegetation photosynthesis and respiration model （VPRM） and improve the calculation of NEE to benefit regional modeling of CO2. Methods Two schemes are examined in optimization of the parameters in VPRM. Two years CO2 flux and me- teorological observational data in 2010-2011 at the Qianyanzhou （QYZ） eddy tower site are used to determine the parameters in VPRM and another full year flux observational data in 2012 are used to evaluate the model perfor- mance. Several statistics metrics are calculated to evaluate the model performance on NEE simulations. Important findings The results indicate, traditional method with Michaelis-Menten equation is not suitable to determine the parameters of VPRM, whereas the method with parameters retrieved from the VPRM calculation equation provides much more reasonable results. The parameter of maximum light use efficiency （λ） is critical for the VPRM calculation of NEE. Our result is larger than the typical value of C3 plant （1/6）, but consistent with the other studies. Using the optimized parameters, VPRM is able to capture NEE variations for different seasons. The statistics calculation with one-year NEE simulation shows that, the mean bias is -0.86 pmol.m-2-s-1 and correla- tion coefficient is 0.72. Overall, the VPRM performs much better in growing season than the non-growing season when the peak value of NEE is underestimated by 52%. The VPRM simulated NEE shows better agreement with observations on sunny days than rainy or cloudy days.