Simultaneous assimilation of satellite and eddy covariance data for improving terrestrial water and carbon simulations at a semi-arid woodland site in Botswana
Simultaneous assimilation of satellite and eddy covariance data for improving terrestrial water and carbon simulations at a semi-arid woodland site in Botswana
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DOI：
10.5194/bg-10-789-2013
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发表时间：
2012-03
期刊：
影响因子：
4.9
通讯作者：
Tomomi Kato;W. Knorr;M. Scholze;E. Veenendaal;T. Kaminski;J. Kattge;N. Gobron
中科院分区：
文献类型：
--
作者：
Tomomi Kato;W. Knorr;M. Scholze;E. Veenendaal;T. Kaminski;J. Kattge;N. Gobron
半干旱林地的陆地生产力极易受到降水变化的影响，半干旱林地是全球水和碳循环的一个重要因素。在这里，我们使用的碳循环数据同化系统（CCDAS），调查控制生态和水文活动的半干旱稀树草原林地网站在马翁，博茨瓦纳的关键参数。利用涡度相关资料和海洋观测宽视场传感器（SeaWiFS）资料，分别和同时对陆地生态系统模式的24个生态水文过程参数进行了优化。同化的数据流LHF和FAPAR的2000年和2001年导致改进的协议之间的测量和模拟量不仅LHF和FAPAR，而且光合CO2吸收。对于同时同化LHF和FAPAR，所有参数的平均不确定性降低（相对于先验）为14.9%，仅同化LHF为8.5%，仅同化FAPAR为6.1%。具有最高不确定性降低的参数集在仅同化FAPAR或仅同化LHF之间是相似的。最高的不确定性减少所有三种情况下被发现的参数量化最大植物有效土壤水分。这表明，不仅LHF，而且卫星派生的FAPAR数据可以用来约束和间接观测水文量。
Terrestrial productivity in semi-arid woodlands is strongly susceptible to changes in precipitation, and semi- arid woodlands constitute an important element of the global water and carbon cycles. Here, we use the Carbon Cycle Data Assimilation System (CCDAS) to investigate the key parameters controlling ecological and hydrological activities for a semi-arid savanna woodland site in Maun, Botswana. Twenty-four eco-hydrological process parameters of a terres- trial ecosystem model are optimized against two data streams separately and simultaneously: daily averaged latent heat flux (LHF) derived from eddy covariance measurements, and decadal fraction of absorbed photosynthetically active radia- tion (FAPAR) derived from the Sea-viewing Wide Field-of- view Sensor (SeaWiFS). Assimilation of both data streams LHF and FAPAR for the years 2000 and 2001 leads to improved agreement be- tween measured and simulated quantities not only for LHF and FAPAR, but also for photosynthetic CO2 uptake. The mean uncertainty reduction (relative to the prior) over all pa- rameters is 14.9 % for the simultaneous assimilation of LHF and FAPAR, 8.5 % for assimilating LHF only, and 6.1 % for assimilating FAPAR only. The set of parameters with the highest uncertainty reduction is similar between assimilating only FAPAR or only LHF. The highest uncertainty reduction for all three cases is found for a parameter quantifying max- imum plant-available soil moisture. This indicates that not only LHF but also satellite-derived FAPAR data can be used to constrain and indirectly observe hydrological quantities.