文章摘要
刘凤山, 林辉, 林兴生, 罗海凌, 林占熺.基于气象条件的巨菌草蒸散发动态研究Journal of Water Resources and Water Engineering[J].,2018,29(3):243-248
基于气象条件的巨菌草蒸散发动态研究
Dynamic study on the evapotranspiration of Giant Juncao based on meteorological condition
  
DOI:10.11705/j.issn.1672-643X.2018.03.41
中文关键词: 巨菌草  蒸散发  气象  水分利用  蒸散发动态
英文关键词: Giant JunCao  evapotranspiration  meteorology  water utilization  evapotranspiration dynamic
基金项目:福建省自然科学基金 (2015J01153)
Author NameAffiliation
LIU Fengshan, LIN Hui, LIN Xingsheng, LUO Hailing, LIN Zhanxi (福建农林大学 国家菌草工程技术研究中心 福建 福州 350002) 
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中文摘要:
      巨菌草是一种已经并将继续在中国干旱和半干旱地区广泛推广的草种,其蒸散发(ET)动态是目前关注较多但仍未解决的问题,制约了灌溉制度、节水途径和潜力、抗旱性能提升以及产量预测等相关主题的发展。基于盆栽控制试验,利用SiB2模型模拟和回归分析方法揭示了巨菌草蒸散发的主要过程及影响因素。结果表明:受气象因素的影响,巨菌草ET在0.5~6.9 mm/d的范围内波动。其中,ET季节动态与温度、日照时数和饱和水汽压差的关系呈先增后减的单峰关系;随相对湿度、短波辐射和净辐射的增大,ET分别线性降低、增大和增大;连续晴天和连续阴天条件下,ET日动态主要受短波辐射和净辐射的正相关影响。利用气象因素建立的多元回归关系,能够解释99%巨菌草ET的波动,总体误差为3.38±7.64 mm。
英文摘要:
      Giant JunCao (Pennisetum giganteum. sp.) is a kind of grass that has been and will continue be widely planted in arid and semi-arid areas of China. The evapotranspiration (ET) dynamic is one of the key concern and unsolved problem, which further constrains the development of topics including irrigation program, water-saving methods and potential, drought resistance performance, and yield prediction. Based on pot control experiment of Giant JunCao, the main process and influence factors of ET were revealed based on SiB2 simulation and regression analysis. Results showed that ET of Giant JunCao, which was dominated by meteorological condition, fluctuated within the range of 0.5 ~ 6.9 mm/day. Among them, ET exhibited unimodal relationship (first enhanced and then reduced) with air temperature, sunshine duration, and vapor pressure deficit, and linearly decreased, increased, and increased with the boost of relative humidity, short-wave radiation, and net radiation, respectively. ET showed different daily change patterns in successive sunny and cloudy days, and mainly affected by short-wave radiation and net radiation. The established multivariate regression relationship between ET of Giant JunCao and meteorological data could explain 99% variance of ET with an error of 3.38±7.64 mm.
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