光能利用效率(light use efficiency,LUE)是指初级生产力与植被冠层吸收的光合有效辐射(absorbed photosynthetically active radiation,APAR)之比,对LUE的准确定量化模拟是定量化模拟初级生产力的基础。研究利用一个基于通量观测的LUE模型(EC-LUE)模拟了2004—2005年藏北高寒草甸的LUE,该模型的参数只有蒸散比(Evaporative Fraction,EF)和气温(air temperature,Ta),EF和Ta分别为最大光能利用效率(maximum light use efficiency,LUEmax)的水分和温度胁迫因子,在研究中LUEmax取0.85 g C/MJ。EF和Ta对LUEmax的胁迫作用存在两种方式:连乘方式和最小限制因子方式,这两种方式模拟的光能利用效率分别记为LUEmultipEC和LUEminEC,并与通量观测数据估算的LUE(LUEEC)进行了比较。结果表明,LUEminEC显著高估了LUEEC,而LUEEC和LUEmultipEC差异不显著;LUEmultipEC和LUEminEC分别解释了89%以上LUEEC的季节变化;EF显著地解释了土壤表层含水量、 比湿,且在一定程度上解释了相对湿度的季节变化;相对于水分胁迫因子,温度胁迫因子更能够解释LUEEC的季节变化。因此,EC-LUE模型可以定量化高寒草甸LUE的季节变化,同时EF可以定量化高寒草甸生态系统水分状况的季节变化。
Light use efficiency (LUE) is defined as the ratio of primary production and absorbed light energy by vegetation canopy. The quantifying modeling of primary production is based on the quantifying modeling of LUE. A light use efficiency model developed from eddy covariance (EC) measurements, called EC-LUE, was used to model LUE of alpine meadows on Northern Tibetan Plateau in 2004-2005. The EC-LUE is driven by evaporative fraction (EF) and air temperature (Ta). EF and Ta were the water attenuation scalar (Wscalar) and temperature attenuation scalar (Tscalar) of maximum light use efficiency (LUEmax), respectively. In this study, LUEmax was set to be 0.85 g C/MJ. The integrated attenuation effect of Tscalar and Wscalar on LUEmax could be multiplied or following the Liebig's law. The LUE values simulated by the two approaches were labeled by LUEmultipEC and LUEminEC. The LUE derived from eddy covariance measurements was labeled by LUEEC. The LUEminECwas significantly larger than LUEEC, but the difference between LUEEC and LUEmultipEC was not significant. LUEmultipEC and LUEminEC explained both significantly above 89% seasonal changes of LUEEC. EF significantly explained soil water content (SW) at the depths of 0.05 m and 0.10 m, and specific humidity. Besides, EF also explained relative humidity to some extent. Compared to Wscalar, Tscalar might explain more seasonal variations of LUE based on correlation analysis and multiple stepwise linear regression analysis. Therefore, EC-LUE model could quantify the seasonal change of LUE and EF could quantify the seasonal change of environmental water for alpine meadows.
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