基于叶面积指数改进的直角双曲线模型在玉米农田生态系统中的应用  

LAI-based photosynthetic light response model and its application in a rainfed maize ecosystem

作　　者：孙敬松[1,2] 周广胜[3,1] 

机构地区：[1]中国科学院植物研究所植被与环境变化国家重点实验室,北京100093 [2]中国科学院研究生院,北京100049 [3]中国气象科学研究院,北京100081

出　　处：《生态学报》2013年第7期2182-2188,共7页Acta Ecologica Sinica

基　　金：公益性行业(农业)科研专项经费项目(200903003);国家重点基础研究发展计划资助(2010CB951303)

摘　　要：一般认为,生态系统的总初级生产力(GPP)对光合有效辐射(LAI)的响应曲线可以用直角双曲线来描述。研究表明,在不同的生长季进行模拟,模拟的直角双曲线的两个参数Amax和α值不同。为消除模型参数季节变化对模拟结果的影响,直角双曲线模型通常应用于较短的时间尺度(如半月、10d或5d),然而,这种在较短的时间尺度上进行模拟的方法过于繁琐,并且当通量数据缺失过多时,在短的时间模拟窗口上,少量的数据不足以拟合直角双曲线模型。在这种情况下,无法利用直角双曲线模型对生态系统的GPP进行准确的模拟,或者对缺失的碳通量数据进行插补。以玉米农田生态系统为例,旨在阐明生态系统的环境因子和生物因子在不同生长季对直角双曲线模型中两个参数Amax和α值的影响。结果表明,Amax与LAI具有显著的直线关系:Amax=a LAI+b(a=0.64,b=0.15,R=0.74,P=0.002)。据此我们对直角双曲线模型进行了改进,用以预测半小时尺度的玉米农田生态系统GPP。与未改进的直角双曲线模型进行比较,在整个生长季进行模拟,改进的直角双曲线模型明显提高了模拟的精度;当在较短的时间窗口上进行模拟(半月时间尺度),改进的直角双曲线模型与之有着相似的精度。利用改进的双曲线模型不仅可以非常简捷地对生态系统GPP进行模拟,而且可以解释直角双曲线模型参数Amax值的连续变化,尤其是,当涡相关观测数据大量缺失时,可以很方便并且较为准确地插补缺失数据。Photosynthetic light response could be expressed as a rectangular hyperbola curve with the fixed parameters AreaX and a. Seasonal variations of Am and a were observed among different ecosystems. In order to eliminate the effects of seasonal variations of Areax and a on simulated result, rectangular hyperbola model was usually fitted at shorter time intervals （e. g. half-month, 10-days, and 5-days）. However, this method is tedious and non-mechanism, especially at a shorter time intervals, small amounts of carbon flux or climate data are not enough for simulating accurately rectangular hyperbola model. In this study we tried to elaborate the effects of biotic and abiotic factors on the parameters （Amax and a） as an example of rainfed maize ecosystem. Biotic and abiotic factors may affect the seasonal dynamics of parameters c and A.,.In order to understand mechanism for the influence of these factors on parameters of model, Multiple regression between seasonal variations of parameters （c and A ax ） and ahiotic factors （e. g. air temperature, soil temperature, relative humidity, soil water content, solar radiation, air vapor pressure deficits（VPD） ） and biotic factor （LAI） were evaluated by stepwise regression analysis. The results showed that there was a significant linear relationship between Am~ and LAI, and LAI is a main factor affecting seasonal variations of Amax. The relationship between Amax and LAI could be expressed as Amax = a LAI ＋ b （a 0.64 b 0.15 R 0.74, P 0. 002）. Thus, a modified model GPP t^PAR（aLAI＋b） = , = , = = - was aPAR＋（aLAI＋b） developed to estimate half-hourly canopy gross primary production （GPP） in maize ecosystem. LAI changed rapidly in a rapid growth phase, but its observed frequency in our experiments was low （at 15--20 days interval ）. Therefore, we introduced a logistic model to interpolate daily LAI by limited observations. After that, we tested the calculated GPP against all the available observed measurements based on three differ

关 键 词：玉米 直角双曲线改进模型 叶面积指数 GPP 

分 类 号：S513[农业科学—作物学]