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作 者:林晓凤 陈报章[3,4] Lin Xiaofeng;Chen Baozhang(College of Harbour and Coastal Engineering,Jimei University,Xiamen 361021,Fujian,China;National Geographic Con-ditions Monitoring Research Center,Jimei University,Xiamen 361021,Fujian,China;State Key Laboratory of Resources and Environment Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China)
机构地区:[1]集美大学港口与海岸工程学院,福建厦门361021 [2]集美大学地理国情监测研究中心,福建厦门361021 [3]中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京100101 [4]中国科学院大学,北京100049
出 处:《地理科学》2022年第7期1260-1271,共12页Scientia Geographica Sinica
基 金:福建省自然科学基金(2021J05169);国家重点研发专项(2018YFA0606001,2017YFA0604302)资助。
摘 要:选取包含9种植被功能型的15个通量塔观测数据(60站点年),采用控制变量法,分别在月尺度和年尺度上对25℃时最大羧化速率(V_(cmax25))进行优化,并利用未参与参数反演的站点年数据集定量评估这2种参数化方案的模拟效果。研究发现:①在参数的季节变异方面:9种植被功能型的V_(cmax25)均呈现明显的季节性波动。V_(cmax25)的波动幅度为:冬季>秋季>春季>夏季,不同植被类型季节变化幅度相差不大,而寒带植被V_(cmax25)季节变化幅度接近温带植被的2倍;②对生态系统初级生产力(GPP)估算的影响方面:V_(cmax25)季节性参数化方案显著提高了GPP的模拟能力和模拟精度,其中森林和灌木冬季提升最显著(R2提高了35.7%,RMSE降低了23.24%),春秋季次之,夏季最小。然而,对于C3草地,DLM无论采取V_(cmax25)季节性参数化方案还是年尺度参数化方案,均为系统低估GPP。The key photosynthetic parameter of maximum carboxylation rate(V_(cmax25))in Dynamic Land Sur-face Model(DLM)was parameterized at monthly and yearly time scales,respectively,for 15 flux towers(60 site-years data)including nine vegetation functional types(PFTs)by using variable-controlling iteration meth-od.The GPP simulation results of the two parameterization schemes are compared and analyzed by using the observation data of the remaining station years not involved in parameterization.The results indicate that:1)the V_(cmax25) varied seasonally obviously for all PFTs.Specifically,the variation of V_(cmax25) was largest in winter and least in summer.The fluctuation of V_(cmax25) was similar among different biomes and was nearly twice in boreal climate region as much as that in temperate climate region.2)The accuracy of GPP estimation using the seasonal parameterization scheme of V_(cmax25) was obviously improved,among which the forest and shrub in-creased the most significantly in winter(R2 increased by 35.7%,RMSE decreased by 23.24%),followed by spring and autumn,and the least in summer.Even with seasonal fluctuation of parameter considered,DLM-FvCB still difficult well capture the GPP variations for C3 grassland.This study suggests an important consider-ation of temporal parameterization in further improvement of modelling GPP in land-surface model.
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